R/gen-namespace-docs.R

In torch: Tensors and Neural Networks with 'GPU' Acceleration

#' Abs
#'
#' @section abs(input) -> Tensor :
#'
#' Computes the element-wise absolute value of the given `input` tensor.
#' 
#' \deqn{
#'     \mbox{out}_{i} = |\mbox{input}_{i}|
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_abs
#'
#' @export
NULL


#' Angle
#'
#' @section angle(input) -> Tensor :
#'
#' Computes the element-wise angle (in radians) of the given `input` tensor.
#' 
#' \deqn{
#'     \mbox{out}_{i} = angle(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_angle
#'
#' @export
NULL


#' Real
#'
#' @section real(input) -> Tensor :
#'
#' Returns the real part of the `input` tensor. If
#' `input` is a real (non-complex) tensor, this function just
#' returns it.
#' 
#' @section Warning:
#'     Not yet implemented for complex tensors.
#' 
#' \deqn{
#'     \mbox{out}_{i} = real(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_real
#'
#' @export
NULL


#' Imag
#'
#' @section imag(input) -> Tensor :
#'
#' Returns the imaginary part of the `input` tensor.
#' 
#' @section Warning:
#'     Not yet implemented.
#' 
#' \deqn{
#'     \mbox{out}_{i} = imag(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_imag
#'
#' @export
NULL


#' Conj
#'
#' @section conj(input) -> Tensor :
#'
#' Computes the element-wise conjugate of the given `input` tensor.
#' 
#' \deqn{
#'     \mbox{out}_{i} = conj(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_conj
#'
#' @export
NULL


#' Acos
#'
#' @section acos(input) -> Tensor :
#'
#' Returns a new tensor with the arccosine  of the elements of `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \cos^{-1}(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_acos
#'
#' @export
NULL


#' Avg_pool1d
#'
#' @section avg_pool1d(input, kernel_size, stride=NULL, padding=0, ceil_mode=FALSE, count_include_pad=TRUE) -> Tensor :
#'
#' Applies a 1D average pooling over an input signal composed of several
#' input planes.
#' 
#' See [nn_avg_pool1d()] for details and output shape.
#'
#'
#' @param self input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}
#' @param kernel_size the size of the window. Can be a single number or a tuple `(kW,)`
#' @param stride the stride of the window. Can be a single number or a tuple `(sW,)`. Default: `kernel_size`
#' @param padding implicit zero paddings on both sides of the input. Can be a single number or a tuple `(padW,)`. Default: 0
#' @param ceil_mode when `TRUE`, will use `ceil` instead of `floor` to compute the output shape. Default: `FALSE`
#' @param count_include_pad when `TRUE`, will include the zero-padding in the averaging calculation. Default: `TRUE`
#'
#' @name torch_avg_pool1d
#'
#' @export
NULL


#' Adaptive_avg_pool1d
#'
#' @section adaptive_avg_pool1d(input, output_size) -> Tensor :
#'
#' Applies a 1D adaptive average pooling over an input signal composed of
#' several input planes.
#' 
#' See [nn_adaptive_avg_pool1d()] for details and output shape.
#'
#' @param self the input tensor
#' @param output_size the target output size (single integer)
#'
#' @name torch_adaptive_avg_pool1d
#'
#' @export
NULL


#' Add
#'
#' @section add(input, other, out=NULL) :
#'
#' Adds the scalar `other` to each element of the input `input`
#' and returns a new resulting tensor.
#' 
#' \deqn{
#'     \mbox{out} = \mbox{input} + \mbox{other}
#' }
#' If `input` is of type FloatTensor or DoubleTensor, `other` must be
#' a real number, otherwise it should be an integer.
#'
#' @section add(input, other, *, alpha=1, out=NULL) :
#'
#' Each element of the tensor `other` is multiplied by the scalar
#' `alpha` and added to each element of the tensor `input`.
#' The resulting tensor is returned.
#' 
#' The shapes of `input` and `other` must be
#' broadcastable .
#' 
#' \deqn{
#'     \mbox{out} = \mbox{input} + \mbox{alpha} \times \mbox{other}
#' }
#' If `other` is of type FloatTensor or DoubleTensor, `alpha` must be
#' a real number, otherwise it should be an integer.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param other (Tensor/Number) the second input tensor/number.
#' @param alpha (Number) the scalar multiplier for `other`
#'
#' @name torch_add
#'
#' @export
NULL


#' Addmv
#'
#' @section addmv(input, mat, vec, *, beta=1, alpha=1, out=NULL) -> Tensor :
#'
#' Performs a matrix-vector product of the matrix `mat` and
#' the vector `vec`.
#' The vector `input` is added to the final result.
#' 
#' If `mat` is a \eqn{(n \times m)} tensor, `vec` is a 1-D tensor of
#' size `m`, then `input` must be
#' broadcastable  with a 1-D tensor of size `n` and
#' `out` will be 1-D tensor of size `n`.
#' 
#' `alpha` and `beta` are scaling factors on matrix-vector product between
#' `mat` and `vec` and the added tensor `input` respectively.
#' 
#' \deqn{
#'     \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{mat} \mathbin{@} \mbox{vec})
#' }
#' For inputs of type `FloatTensor` or `DoubleTensor`, arguments `beta` and
#' `alpha` must be real numbers, otherwise they should be integers
#'
#'
#' @param self (Tensor) vector to be added
#' @param mat (Tensor) matrix to be multiplied
#' @param vec (Tensor) vector to be multiplied
#' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
#' @param alpha (Number, optional) multiplier for \eqn{mat @ vec} (\eqn{\alpha})
#' 
#'
#' @name torch_addmv
#'
#' @export
NULL


#' Addr
#'
#' @section addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -> Tensor :
#'
#' Performs the outer-product of vectors `vec1` and `vec2`
#' and adds it to the matrix `input`.
#' 
#' Optional values `beta` and `alpha` are scaling factors on the
#' outer product between `vec1` and `vec2` and the added matrix
#' `input` respectively.
#' 
#' \deqn{
#'     \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{vec1} \otimes \mbox{vec2})
#' }
#' If `vec1` is a vector of size `n` and `vec2` is a vector
#' of size `m`, then `input` must be
#' broadcastable  with a matrix of size
#' \eqn{(n \times m)} and `out` will be a matrix of size
#' \eqn{(n \times m)}.
#' 
#' For inputs of type `FloatTensor` or `DoubleTensor`, arguments `beta` and
#' `alpha` must be real numbers, otherwise they should be integers
#'
#'
#' @param self (Tensor) matrix to be added
#' @param vec1 (Tensor) the first vector of the outer product
#' @param vec2 (Tensor) the second vector of the outer product
#' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
#' @param alpha (Number, optional) multiplier for \eqn{\mbox{vec1} \otimes \mbox{vec2}} (\eqn{\alpha})
#' 
#'
#' @name torch_addr
#'
#' @export
NULL


#' Allclose
#'
#' @section allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -> bool :
#'
#' This function checks if all `input` and `other` satisfy the condition:
#' 
#' \deqn{
#'     \vert \mbox{input} - \mbox{other} \vert \leq \mbox{atol} + \mbox{rtol} \times \vert \mbox{other} \vert
#' }
#' elementwise, for all elements of `input` and `other`. The behaviour of this function is analogous to
#' `numpy.allclose <https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html>`_
#'
#'
#' @param self (Tensor) first tensor to compare
#' @param other (Tensor) second tensor to compare
#' @param atol (float, optional) absolute tolerance. Default: 1e-08
#' @param rtol (float, optional) relative tolerance. Default: 1e-05
#' @param equal_nan (bool, optional) if `TRUE`, then two `NaN` s will be compared as equal. Default: `FALSE`
#'
#' @name torch_allclose
#'
#' @export
NULL


#' Arange
#'
#' @section arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
#'
#' Returns a 1-D tensor of size \eqn{\left\lceil \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rceil}
#' with values from the interval `[start, end)` taken with common difference
#' `step` beginning from `start`.
#' 
#' Note that non-integer `step` is subject to floating point rounding errors when
#' comparing against `end`; to avoid inconsistency, we advise adding a small epsilon to `end`
#' in such cases.
#' 
#' \deqn{
#'     \mbox{out}_{{i+1}} = \mbox{out}_{i} + \mbox{step}
#' }
#'
#'
#' @param start (Number) the starting value for the set of points. Default: `0`.
#' @param end (Number) the ending value for the set of points
#' @param step (Number) the gap between each pair of adjacent points. Default: `1`.
#' 
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). If `dtype` is not given, infer the data type from the other input        arguments. If any of `start`, `end`, or `stop` are floating-point, the        `dtype` is inferred to be the default dtype, see        `~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to        be `torch.int64`.
#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#'
#' @name torch_arange
#'
#' @export
NULL


#' Argmax
#'
#' @section argmax(input) -> LongTensor :
#'
#' Returns the indices of the maximum value of all elements in the `input` tensor.
#' 
#' This is the second value returned by `torch_max`. See its
#' documentation for the exact semantics of this method.
#'
#' @section argmax(input, dim, keepdim=False) -> LongTensor :
#'
#' Returns the indices of the maximum values of a tensor across a dimension.
#' 
#' This is the second value returned by `torch_max`. See its
#' documentation for the exact semantics of this method.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int) the dimension to reduce. If `NULL`, the argmax of the flattened input is returned.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Ignored if `dim=NULL`.
#'
#' @name torch_argmax
#'
#' @export
NULL


#' Argmin
#'
#' @section argmin(input) -> LongTensor :
#'
#' Returns the indices of the minimum value of all elements in the `input` tensor.
#' 
#' This is the second value returned by `torch_min`. See its
#' documentation for the exact semantics of this method.
#'
#' @section argmin(input, dim, keepdim=False, out=NULL) -> LongTensor :
#'
#' Returns the indices of the minimum values of a tensor across a dimension.
#' 
#' This is the second value returned by `torch_min`. See its
#' documentation for the exact semantics of this method.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int) the dimension to reduce. If `NULL`, the argmin of the flattened input is returned.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Ignored if `dim=NULL`.
#'
#' @name torch_argmin
#'
#' @export
NULL


#' As_strided
#'
#' @section as_strided(input, size, stride, storage_offset=0) -> Tensor :
#'
#' Create a view of an existing `torch_Tensor` `input` with specified
#' `size`, `stride` and `storage_offset`.
#' 
#' @section Warning:
#'     More than one element of a created tensor may refer to a single memory
#'     location. As a result, in-place operations (especially ones that are
#'     vectorized) may result in incorrect behavior. If you need to write to
#'     the tensors, please clone them first.
#' 
#'     Many PyTorch functions, which return a view of a tensor, are internally
#'     implemented with this function. Those functions, like
#'     `torch_Tensor.expand`, are easier to read and are therefore more
#'     advisable to use.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param size (tuple or ints) the shape of the output tensor
#' @param stride (tuple or ints) the stride of the output tensor
#' @param storage_offset (int, optional) the offset in the underlying storage of the output tensor
#'
#' @name torch_as_strided
#'
#' @export
NULL


#' Asin
#'
#' @section asin(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the arcsine  of the elements of `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \sin^{-1}(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_asin
#'
#' @export
NULL


#' Atan
#'
#' @section atan(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the arctangent  of the elements of `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \tan^{-1}(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_atan
#'
#' @export
NULL


#' Baddbmm
#'
#' @section baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor :
#'
#' Performs a batch matrix-matrix product of matrices in `batch1`
#' and `batch2`.
#' `input` is added to the final result.
#' 
#' `batch1` and `batch2` must be 3-D tensors each containing the same
#' number of matrices.
#' 
#' If `batch1` is a \eqn{(b \times n \times m)} tensor, `batch2` is a
#' \eqn{(b \times m \times p)} tensor, then `input` must be
#' broadcastable  with a
#' \eqn{(b \times n \times p)} tensor and `out` will be a
#' \eqn{(b \times n \times p)} tensor. Both `alpha` and `beta` mean the
#' same as the scaling factors used in `torch_addbmm`.
#' 
#' \deqn{
#'     \mbox{out}_i = \beta\ \mbox{input}_i + \alpha\ (\mbox{batch1}_i \mathbin{@} \mbox{batch2}_i)
#' }
#' For inputs of type `FloatTensor` or `DoubleTensor`, arguments `beta` and
#' `alpha` must be real numbers, otherwise they should be integers.
#'
#'
#' @param self (Tensor) the tensor to be added
#' @param batch1 (Tensor) the first batch of matrices to be multiplied
#' @param batch2 (Tensor) the second batch of matrices to be multiplied
#' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
#' @param alpha (Number, optional) multiplier for \eqn{\mbox{batch1} \mathbin{@} \mbox{batch2}} (\eqn{\alpha})
#' 
#'
#' @name torch_baddbmm
#'
#' @export
NULL


#' Bartlett_window
#'
#' @section bartlett_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
#'
#' Bartlett window function.
#' 
#' \deqn{
#'     w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \left\{ \begin{array}{ll}
#'         \frac{2n}{N - 1} & \mbox{if } 0 \leq n \leq \frac{N - 1}{2} \\
#'         2 - \frac{2n}{N - 1} & \mbox{if } \frac{N - 1}{2} < n < N \\
#'     \end{array}
#'     \right. ,
#' }
#' where \eqn{N} is the full window size.
#' 
#' The input `window_length` is a positive integer controlling the
#' returned window size. `periodic` flag determines whether the returned
#' window trims off the last duplicate value from the symmetric window and is
#' ready to be used as a periodic window with functions like
#' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in
#' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
#' `torch_bartlett_window(L, periodic=TRUE)` equal to
#' `torch_bartlett_window(L + 1, periodic=False)[:-1])`.
#' 
#' @note
#'     If `window_length` \eqn{=1}, the returned window contains a single value 1.
#'
#'
#' @param window_length (int) the size of returned window
#' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          `torch_strided` (dense layout) is supported.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#'
#' @name torch_bartlett_window
#'
#' @export
NULL


#' Bernoulli
#'
#' @section bernoulli(input, *, generator=NULL, out=NULL) -> Tensor :
#'
#' Draws binary random numbers (0 or 1) from a Bernoulli distribution.
#' 
#' The `input` tensor should be a tensor containing probabilities
#' to be used for drawing the binary random number.
#' Hence, all values in `input` have to be in the range:
#' \eqn{0 \leq \mbox{input}_i \leq 1}.
#' 
#' The \eqn{\mbox{i}^{th}} element of the output tensor will draw a
#' value \eqn{1} according to the \eqn{\mbox{i}^{th}} probability value given
#' in `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} \sim \mathrm{Bernoulli}(p = \mbox{input}_{i})
#' }
#' The returned `out` tensor only has values 0 or 1 and is of the same
#' shape as `input`.
#' 
#' `out` can have integral `dtype`, but `input` must have floating
#' point `dtype`.
#'
#'
#' @param self (Tensor) the input tensor of probability values for the Bernoulli 
#'   distribution
#' @param p (Number) a probability value. If `p` is passed than it's used instead of 
#'   the values in `self` tensor.
#' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling
#' 
#'
#' @name torch_bernoulli
#'
#' @export
NULL


#' Bincount
#'
#' @section bincount(input, weights=NULL, minlength=0) -> Tensor :
#'
#' Count the frequency of each value in an array of non-negative ints.
#' 
#' The number of bins (size 1) is one larger than the largest value in
#' `input` unless `input` is empty, in which case the result is a
#' tensor of size 0. If `minlength` is specified, the number of bins is at least
#' `minlength` and if `input` is empty, then the result is tensor of size
#' `minlength` filled with zeros. If `n` is the value at position `i`,
#' `out[n] += weights[i]` if `weights` is specified else
#' `out[n] += 1`.
#' 
#' .. include:: cuda_deterministic.rst
#'
#'
#' @param self (Tensor) 1-d int tensor
#' @param weights (Tensor) optional, weight for each value in the input tensor.        Should be of same size as input tensor.
#' @param minlength (int) optional, minimum number of bins. Should be non-negative.
#'
#' @name torch_bincount
#'
#' @export
NULL


#' Bitwise_not
#'
#' @section bitwise_not(input, out=NULL) -> Tensor :
#'
#' Computes the bitwise NOT of the given input tensor. The input tensor must be of
#' integral or Boolean types. For bool tensors, it computes the logical NOT.
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_bitwise_not
#'
#' @export
NULL


#' Logical_not
#'
#' @section logical_not(input, out=NULL) -> Tensor :
#'
#' Computes the element-wise logical NOT of the given input tensor. If not specified, the output tensor will have the bool
#' dtype. If the input tensor is not a bool tensor, zeros are treated as `FALSE` and non-zeros are treated as `TRUE`.
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_logical_not
#'
#' @export
NULL


#' Logical_xor
#'
#' @section logical_xor(input, other, out=NULL) -> Tensor :
#'
#' Computes the element-wise logical XOR of the given input tensors. Zeros are treated as `FALSE` and nonzeros are
#' treated as `TRUE`.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param other (Tensor) the tensor to compute XOR with
#' 
#'
#' @name torch_logical_xor
#'
#' @export
NULL


#' Logical_and
#'
#' @section logical_and(input, other, out=NULL) -> Tensor :
#'
#' Computes the element-wise logical AND of the given input tensors. Zeros are treated as `FALSE` and nonzeros are
#' treated as `TRUE`.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param other (Tensor) the tensor to compute AND with
#' 
#'
#' @name torch_logical_and
#'
#' @export
NULL


#' Logical_or
#'
#' @section logical_or(input, other, out=NULL) -> Tensor :
#'
#' Computes the element-wise logical OR of the given input tensors. Zeros are treated as `FALSE` and nonzeros are
#' treated as `TRUE`.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param other (Tensor) the tensor to compute OR with
#' 
#'
#' @name torch_logical_or
#'
#' @export
NULL


#' Blackman_window
#'
#' @section blackman_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
#'
#' Blackman window function.
#' 
#' \deqn{
#'     w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right)
#' }
#' where \eqn{N} is the full window size.
#' 
#' The input `window_length` is a positive integer controlling the
#' returned window size. `periodic` flag determines whether the returned
#' window trims off the last duplicate value from the symmetric window and is
#' ready to be used as a periodic window with functions like
#' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in
#' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
#' `torch_blackman_window(L, periodic=TRUE)` equal to
#' `torch_blackman_window(L + 1, periodic=False)[:-1])`.
#' 
#' @note
#'     If `window_length` \eqn{=1}, the returned window contains a single value 1.
#'
#'
#' @param window_length (int) the size of returned window
#' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          `torch_strided` (dense layout) is supported.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#'
#' @name torch_blackman_window
#'
#' @export
NULL


#' Bmm
#'
#' @section bmm(input, mat2, out=NULL) -> Tensor :
#'
#' Performs a batch matrix-matrix product of matrices stored in `input`
#' and `mat2`.
#' 
#' `input` and `mat2` must be 3-D tensors each containing
#' the same number of matrices.
#' 
#' If `input` is a \eqn{(b \times n \times m)} tensor, `mat2` is a
#' \eqn{(b \times m \times p)} tensor, `out` will be a
#' \eqn{(b \times n \times p)} tensor.
#' 
#' \deqn{
#'     \mbox{out}_i = \mbox{input}_i \mathbin{@} \mbox{mat2}_i
#' }
#' @note This function does not broadcast .
#'           For broadcasting matrix products, see [`torch_matmul`].
#'
#'
#' @param self (Tensor) the first batch of matrices to be multiplied
#' @param mat2 (Tensor) the second batch of matrices to be multiplied
#' 
#'
#' @name torch_bmm
#'
#' @export
NULL


#' Broadcast_tensors
#'
#' @section broadcast_tensors(tensors) -> List of Tensors :
#'
#' Broadcasts the given tensors according to broadcasting-semantics.
#'
#' @param tensors a list containing any number of tensors of the same type
#'
#' @name torch_broadcast_tensors
#'
#' @export
NULL


#' Cat
#'
#' @section cat(tensors, dim=0, out=NULL) -> Tensor :
#'
#' Concatenates the given sequence of `seq` tensors in the given dimension.
#' All tensors must either have the same shape (except in the concatenating
#' dimension) or be empty.
#' 
#' [`torch_cat`] can be seen as an inverse operation for [torch_split()]
#' and [`torch_chunk`].
#' 
#' [`torch_cat`] can be best understood via examples.
#'
#'
#' @param tensors (sequence of Tensors) any python sequence of tensors of the same type.        Non-empty tensors provided must have the same shape, except in the        cat dimension.
#' @param dim (int, optional) the dimension over which the tensors are concatenated
#' 
#'
#' @name torch_cat
#'
#' @export
NULL


#' Ceil
#'
#' @section ceil(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the ceil of the elements of `input`,
#' the smallest integer greater than or equal to each element.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \left\lceil \mbox{input}_{i} \right\rceil = \left\lfloor \mbox{input}_{i} \right\rfloor + 1
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_ceil
#'
#' @export
NULL


#' Chain_matmul
#'
#' @section TEST :
#'
#' Returns the matrix product of the \eqn{N} 2-D tensors. This product is efficiently computed
#'     using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms
#'     of arithmetic operations (`[CLRS]`_). Note that since this is a function to compute the product, \eqn{N}
#'     needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matrix product is returned.
#'     If \eqn{N} is 1, then this is a no-op - the original matrix is returned as is.
#'
#'
#' @param matrices (Tensors...) a sequence of 2 or more 2-D tensors whose product is to be determined.
#'
#' @name torch_chain_matmul
#'
#' @export
NULL


#' Chunk
#'
#' @section chunk(input, chunks, dim=0) -> List of Tensors :
#'
#' Splits a tensor into a specific number of chunks. Each chunk is a view of
#' the input tensor.
#' 
#' Last chunk will be smaller if the tensor size along the given dimension
#' `dim` is not divisible by `chunks`.
#'
#'
#' @param self (Tensor) the tensor to split
#' @param chunks (int) number of chunks to return
#' @param dim (int) dimension along which to split the tensor
#'
#' @name torch_chunk
#'
#' @export
NULL


#' Clamp
#'
#' @section clamp(input, min, max, out=NULL) -> Tensor :
#'
#' Clamp all elements in `input` into the range `[` `min`, `max` `]` and return
#' a resulting tensor:
#' 
#' \deqn{
#'     y_i = \left\{ \begin{array}{ll}
#'         \mbox{min} & \mbox{if } x_i < \mbox{min} \\
#'         x_i & \mbox{if } \mbox{min} \leq x_i \leq \mbox{max} \\
#'         \mbox{max} & \mbox{if } x_i > \mbox{max}
#'     \end{array}
#'     \right.
#' }
#' If `input` is of type `FloatTensor` or `DoubleTensor`, args `min`
#' and `max` must be real numbers, otherwise they should be integers.
#'
#' @section clamp(input, *, min, out=NULL) -> Tensor :
#'
#' Clamps all elements in `input` to be larger or equal `min`.
#' 
#' If `input` is of type `FloatTensor` or `DoubleTensor`, `value`
#' should be a real number, otherwise it should be an integer.
#'
#' @section clamp(input, *, max, out=NULL) -> Tensor :
#'
#' Clamps all elements in `input` to be smaller or equal `max`.
#' 
#' If `input` is of type `FloatTensor` or `DoubleTensor`, `value`
#' should be a real number, otherwise it should be an integer.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param min (Number) lower-bound of the range to be clamped to
#' @param max (Number) upper-bound of the range to be clamped to
#' 
#'
#' @name torch_clamp
#'
#' @export
NULL


#' Conv1d
#'
#' @section conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor :
#'
#' Applies a 1D convolution over an input signal composed of several input
#' planes.
#' 
#' See [nn_conv1d()] for details and output shape.
#'
#'
#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}
#' @param weight filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)}
#' @param bias optional bias of shape \eqn{(\mbox{out\_channels})}. Default: `NULL`
#' @param stride the stride of the convolving kernel. Can be a single number or      a one-element tuple `(sW,)`. Default: 1
#' @param padding implicit paddings on both sides of the input. Can be a      single number or a one-element tuple `(padW,)`. Default: 0
#' @param dilation the spacing between kernel elements. Can be a single number or      a one-element tuple `(dW,)`. Default: 1
#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1
#'
#' @name torch_conv1d
#'
#' @export
NULL


#' Conv2d
#'
#' @section conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor :
#'
#' Applies a 2D convolution over an input image composed of several input
#' planes.
#' 
#' See [nn_conv2d()] for details and output shape.
#'
#'
#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}
#' @param weight filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)}
#' @param bias optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: `NULL`
#' @param stride the stride of the convolving kernel. Can be a single number or a      tuple `(sH, sW)`. Default: 1
#' @param padding implicit paddings on both sides of the input. Can be a      single number or a tuple `(padH, padW)`. Default: 0
#' @param dilation the spacing between kernel elements. Can be a single number or      a tuple `(dH, dW)`. Default: 1
#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1
#'
#' @name torch_conv2d
#'
#' @export
NULL


#' Conv3d
#'
#' @section conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor :
#'
#' Applies a 3D convolution over an input image composed of several input
#' planes.
#' 
#' See [nn_conv3d()] for details and output shape.
#'
#'
#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}
#' @param weight filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)}
#' @param bias optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: NULL
#' @param stride the stride of the convolving kernel. Can be a single number or a      tuple `(sT, sH, sW)`. Default: 1
#' @param padding implicit paddings on both sides of the input. Can be a      single number or a tuple `(padT, padH, padW)`. Default: 0
#' @param dilation the spacing between kernel elements. Can be a single number or      a tuple `(dT, dH, dW)`. Default: 1
#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1
#'
#' @name torch_conv3d
#'
#' @export
NULL


#' Conv_tbc
#'
#' @section TEST :
#'
#' Applies a 1-dimensional sequence convolution over an input sequence.
#' Input and output dimensions are (Time, Batch, Channels) - hence TBC.
#'
#'
#' @param self NA input tensor of shape \eqn{(\mbox{sequence length} \times batch \times \mbox{in\_channels})}
#' @param weight NA filter of shape (\eqn{\mbox{kernel width} \times \mbox{in\_channels} \times \mbox{out\_channels}})
#' @param bias NA bias of shape (\eqn{\mbox{out\_channels}})
#' @param pad NA number of timesteps to pad. Default: 0
#'
#' @name torch_conv_tbc
#'
#' @export
NULL


#' Conv_transpose1d
#'
#' @section conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor :
#'
#' Applies a 1D transposed convolution operator over an input signal
#' composed of several input planes, sometimes also called "deconvolution".
#' 
#' See [nn_conv_transpose1d()]  for details and output shape.
#'
#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}
#' @param weight filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)}
#' @param bias optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL
#' @param stride the stride of the convolving kernel. Can be a single number or a      tuple `(sW,)`. Default: 1
#' @param padding `dilation * (kernel_size - 1) - padding` zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      `(padW,)`. Default: 0
#' @param output_padding additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple `(out_padW)`. Default: 0
#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1
#' @param dilation the spacing between kernel elements. Can be a single number or      a tuple `(dW,)`. Default: 1
#'
#' @name torch_conv_transpose1d
#'
#' @export
NULL


#' Conv_transpose2d
#'
#' @section conv_transpose2d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor :
#'
#' Applies a 2D transposed convolution operator over an input image
#' composed of several input planes, sometimes also called "deconvolution".
#' 
#' See [nn_conv_transpose2d()] for details and output shape.
#'
#'
#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}
#' @param weight filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)}
#' @param bias optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL
#' @param stride the stride of the convolving kernel. Can be a single number or a      tuple `(sH, sW)`. Default: 1
#' @param padding `dilation * (kernel_size - 1) - padding` zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      `(padH, padW)`. Default: 0
#' @param output_padding additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple `(out_padH, out_padW)`.      Default: 0
#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1
#' @param dilation the spacing between kernel elements. Can be a single number or      a tuple `(dH, dW)`. Default: 1
#'
#' @name torch_conv_transpose2d
#'
#' @export
NULL


#' Conv_transpose3d
#'
#' @section conv_transpose3d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor :
#'
#' Applies a 3D transposed convolution operator over an input image
#' composed of several input planes, sometimes also called "deconvolution"
#' 
#' See [nn_conv_transpose3d()] for details and output shape.
#'
#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}
#' @param weight filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)}
#' @param bias optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL
#' @param stride the stride of the convolving kernel. Can be a single number or a      tuple `(sT, sH, sW)`. Default: 1
#' @param padding `dilation * (kernel_size - 1) - padding` zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      `(padT, padH, padW)`. Default: 0
#' @param output_padding additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      `(out_padT, out_padH, out_padW)`. Default: 0
#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1
#' @param dilation the spacing between kernel elements. Can be a single number or      a tuple `(dT, dH, dW)`. Default: 1
#'
#' @name torch_conv_transpose3d
#'
#' @export
NULL


#' Cos
#'
#' @section cos(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the cosine  of the elements of `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \cos(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_cos
#'
#' @export
NULL


#' Cosh
#'
#' @section cosh(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the hyperbolic cosine  of the elements of
#' `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \cosh(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_cosh
#'
#' @export
NULL


#' Cummax
#'
#' @section cummax(input, dim) -> (Tensor, LongTensor) :
#'
#' Returns a namedtuple `(values, indices)` where `values` is the cumulative maximum of
#' elements of `input` in the dimension `dim`. And `indices` is the index
#' location of each maximum value found in the dimension `dim`.
#' 
#' \deqn{
#'     y_i = max(x_1, x_2, x_3, \dots, x_i)
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int) the dimension to do the operation over
#'
#' @name torch_cummax
#'
#' @export
NULL


#' Cummin
#'
#' @section cummin(input, dim) -> (Tensor, LongTensor) :
#'
#' Returns a namedtuple `(values, indices)` where `values` is the cumulative minimum of
#' elements of `input` in the dimension `dim`. And `indices` is the index
#' location of each maximum value found in the dimension `dim`.
#' 
#' \deqn{
#'     y_i = min(x_1, x_2, x_3, \dots, x_i)
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int) the dimension to do the operation over
#'
#' @name torch_cummin
#'
#' @export
NULL


#' Cumprod
#'
#' @section cumprod(input, dim, out=NULL, dtype=NULL) -> Tensor :
#'
#' Returns the cumulative product of elements of `input` in the dimension
#' `dim`.
#' 
#' For example, if `input` is a vector of size N, the result will also be
#' a vector of size N, with elements.
#' 
#' \deqn{
#'     y_i = x_1 \times x_2\times x_3\times \dots \times x_i
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int) the dimension to do the operation over
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to `dtype` before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.
#' 
#'
#' @name torch_cumprod
#'
#' @export
NULL


#' Cumsum
#'
#' @section cumsum(input, dim, out=NULL, dtype=NULL) -> Tensor :
#'
#' Returns the cumulative sum of elements of `input` in the dimension
#' `dim`.
#' 
#' For example, if `input` is a vector of size N, the result will also be
#' a vector of size N, with elements.
#' 
#' \deqn{
#'     y_i = x_1 + x_2 + x_3 + \dots + x_i
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int) the dimension to do the operation over
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to `dtype` before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.
#' 
#'
#' @name torch_cumsum
#'
#' @export
NULL


#' Det
#'
#' @section det(input) -> Tensor :
#'
#' Calculates determinant of a square matrix or batches of square matrices.
#' 
#' @note
#'     Backward through `det` internally uses SVD results when `input` is
#'     not invertible. In this case, double backward through `det` will be
#'     unstable in when `input` doesn't have distinct singular values. See
#'     `~torch.svd` for details.
#'
#'
#' @param self (Tensor) the input tensor of size `(*, n, n)` where `*` is zero or more                batch dimensions.
#'
#' @name torch_det
#'
#' @export
NULL


#' Diag_embed
#'
#' @section diag_embed(input, offset=0, dim1=-2, dim2=-1) -> Tensor :
#'
#' Creates a tensor whose diagonals of certain 2D planes (specified by
#' `dim1` and `dim2`) are filled by `input`.
#' To facilitate creating batched diagonal matrices, the 2D planes formed by
#' the last two dimensions of the returned tensor are chosen by default.
#' 
#' The argument `offset` controls which diagonal to consider:
#' 
#' - If `offset` = 0, it is the main diagonal.
#' - If `offset` > 0, it is above the main diagonal.
#' - If `offset` < 0, it is below the main diagonal.
#' 
#' The size of the new matrix will be calculated to make the specified diagonal
#' of the size of the last input dimension.
#' Note that for `offset` other than \eqn{0}, the order of `dim1`
#' and `dim2` matters. Exchanging them is equivalent to changing the
#' sign of `offset`.
#' 
#' Applying `torch_diagonal` to the output of this function with
#' the same arguments yields a matrix identical to input. However,
#' `torch_diagonal` has different default dimensions, so those
#' need to be explicitly specified.
#'
#'
#' @param self (Tensor) the input tensor. Must be at least 1-dimensional.
#' @param offset (int, optional) which diagonal to consider. Default: 0        (main diagonal).
#' @param dim1 (int, optional) first dimension with respect to which to        take diagonal. Default: -2.
#' @param dim2 (int, optional) second dimension with respect to which to        take diagonal. Default: -1.
#' 
#' @name torch_diag_embed
#'
#' @export
NULL


#' Diagflat
#'
#' @section diagflat(input, offset=0) -> Tensor :
#'
#' - If `input` is a vector (1-D tensor), then returns a 2-D square tensor
#'   with the elements of `input` as the diagonal.
#' - If `input` is a tensor with more than one dimension, then returns a
#'   2-D tensor with diagonal elements equal to a flattened `input`.
#' 
#' The argument `offset` controls which diagonal to consider:
#' 
#' - If `offset` = 0, it is the main diagonal.
#' - If `offset` > 0, it is above the main diagonal.
#' - If `offset` < 0, it is below the main diagonal.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param offset (int, optional) the diagonal to consider. Default: 0 (main        diagonal).
#'
#' @name torch_diagflat
#'
#' @export
NULL


#' Diagonal
#'
#' @section diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor :
#'
#' Returns a partial view of `input` with the its diagonal elements
#' with respect to `dim1` and `dim2` appended as a dimension
#' at the end of the shape.
#' 
#' The argument `offset` controls which diagonal to consider:
#' 
#' - If `offset` = 0, it is the main diagonal.
#' - If `offset` > 0, it is above the main diagonal.
#' - If `offset` < 0, it is below the main diagonal.
#' 
#' Applying `torch_diag_embed` to the output of this function with
#' the same arguments yields a diagonal matrix with the diagonal entries
#' of the input. However, `torch_diag_embed` has different default
#' dimensions, so those need to be explicitly specified.
#'
#'
#' @param self (Tensor) the input tensor. Must be at least 2-dimensional.
#' @param offset (int, optional) which diagonal to consider. Default: 0        (main diagonal).
#' @param dim1 (int, optional) first dimension with respect to which to        take diagonal. Default: 0.
#' @param dim2 (int, optional) second dimension with respect to which to        take diagonal. Default: 1.
#' @param outdim dimension name if `self` is a named tensor.
#'
#' @name torch_diagonal
#'
#' @export
NULL


#' Div
#'
#' @section div(input, other, out=NULL) -> Tensor :
#'
#' Divides each element of the input `input` with the scalar `other` and
#' returns a new resulting tensor.
#' 
#' @section Warning:
#'     Integer division using div is deprecated, and in a future release div will
#'     perform true division like [torch_true_divide()].
#'     Use [torch_floor_divide()] to perform integer division,
#'     instead.
#' 
#' \deqn{
#'     \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}}
#' }
#' If the `torch_dtype` of `input` and `other` differ, the
#' `torch_dtype` of the result tensor is determined following rules
#' described in the type promotion documentation . If
#' `out` is specified, the result must be castable 
#' to the `torch_dtype` of the specified output tensor. Integral division
#' by zero leads to undefined behavior.
#'
#' @section div(input, other, out=NULL) -> Tensor :
#'
#' Each element of the tensor `input` is divided by each element of the tensor
#' `other`. The resulting tensor is returned.
#' 
#' \deqn{
#'     \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}_i}
#' }
#' The shapes of `input` and `other` must be broadcastable
#' . If the `torch_dtype` of `input` and
#' `other` differ, the `torch_dtype` of the result tensor is determined
#' following rules described in the type promotion documentation
#' . If `out` is specified, the result must be
#' castable  to the `torch_dtype` of the
#' specified output tensor. Integral division by zero leads to undefined behavior.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param other (Number) the number to be divided to each element of `input`
#' @param rounding_mode (str, optional) – Type of rounding applied to the result:
#'   * `NULL` - default behavior. Performs no rounding and, if both input and 
#'     other are integer types, promotes the inputs to the default scalar type. 
#'     Equivalent to true division in Python (the / operator) and NumPy’s 
#'     `np.true_divide`.
#'   * "trunc" - rounds the results of the division towards zero. Equivalent to 
#'     C-style integer division.
#'   * "floor" - rounds the results of the division down. Equivalent to floor 
#'     division in Python (the // operator) and NumPy’s `np.floor_divide`.
#'
#' @name torch_div
#'
#' @export
NULL


#' Dot
#'
#' @section dot(input, tensor) -> Tensor :
#'
#' Computes the dot product (inner product) of two tensors.
#' 
#' @note This function does not broadcast .
#'
#' @param self the input tensor
#' @param tensor the other input tensor
#'
#' @name torch_dot
#'
#' @export
NULL


#' Einsum
#'
#' @section einsum(equation, *operands) -> Tensor :
#'
#' This function provides a way of computing multilinear expressions (i.e. sums of products) using the
#' Einstein summation convention.
#'
#' @param equation (string) The equation is given in terms of lower case letters (indices) to be associated           with each dimension of the operands and result. The left hand side lists the operands           dimensions, separated by commas. There should be one index letter per tensor dimension.           The right hand side follows after `->` and gives the indices for the output.           If the `->` and right hand side are omitted, it implicitly defined as the alphabetically           sorted list of all indices appearing exactly once in the left hand side.           The indices not apprearing in the output are summed over after multiplying the operands           entries.           If an index appears several times for the same operand, a diagonal is taken.           Ellipses `...` represent a fixed number of dimensions. If the right hand side is inferred,           the ellipsis dimensions are at the beginning of the output.
#' @param tensors (Tensor) The operands to compute the Einstein sum of.
#' @param path (int) This function uses [opt_einsum](https://optimized-einsum.readthedocs.io/en/stable/) to 
#'   speed up computation or to consume less memory by optimizing contraction order. This optimization 
#'   occurs when there are at least three inputs, since the order does not matter otherwise. 
#'   Note that finding _the_ optimal path is an NP-hard problem, thus, `opt_einsum` relies 
#'   on different heuristics to achieve near-optimal results. If `opt_einsum` is not available, 
#'   the default order is to contract from left to right.
#'   The path argument is used to changed that default, but it should only be set by advanced users.
#'
#' @name torch_einsum
#'
#' @export
NULL


#' Empty
#'
#' @section empty(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, pin_memory=False) -> Tensor :
#'
#' Returns a tensor filled with uninitialized data. The shape of the tensor is
#' defined by the variable argument `size`.
#'
#'
#' @param ... a sequence of integers defining the shape of the output tensor.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#' @param names optional character vector naming each dimension.
#' 
#' @name torch_empty
#'
#' @export
NULL


#' Empty_like
#'
#' @section empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
#'
#' Returns an uninitialized tensor with the same size as `input`.
#' `torch_empty_like(input)` is equivalent to
#' `torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`.
#'
#'
#' @param input (Tensor) the size of `input` will determine size of the output tensor.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: `torch_preserve_format`.
#'
#' @name torch_empty_like
#'
#' @export
NULL


#' Empty_strided
#'
#' @section empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -> Tensor :
#'
#' Returns a tensor filled with uninitialized data. The shape and strides of the tensor is
#' defined by the variable argument `size` and `stride` respectively.
#' `torch_empty_strided(size, stride)` is equivalent to
#' `torch_empty(size).as_strided(size, stride)`.
#' 
#' @section Warning:
#'     More than one element of the created tensor may refer to a single memory
#'     location. As a result, in-place operations (especially ones that are
#'     vectorized) may result in incorrect behavior. If you need to write to
#'     the tensors, please clone them first.
#'
#'
#' @param size (tuple of ints) the shape of the output tensor
#' @param stride (tuple of ints) the strides of the output tensor
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#' @param pin_memory (bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: `FALSE`.
#'
#' @name torch_empty_strided
#'
#' @export
NULL


#' Erf
#'
#' @section erf(input, out=NULL) -> Tensor :
#'
#' Computes the error function of each element. The error function is defined as follows:
#' 
#' \deqn{
#'     \mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_erf
#'
#' @export
NULL


#' Erfc
#'
#' @section erfc(input, out=NULL) -> Tensor :
#'
#' Computes the complementary error function of each element of `input`.
#' The complementary error function is defined as follows:
#' 
#' \deqn{
#'     \mathrm{erfc}(x) = 1 - \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_erfc
#'
#' @export
NULL


#' Exp
#'
#' @section exp(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the exponential of the elements
#' of the input tensor `input`.
#' 
#' \deqn{
#'     y_{i} = e^{x_{i}}
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_exp
#'
#' @export
NULL


#' Expm1
#'
#' @section expm1(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the exponential of the elements minus 1
#' of `input`.
#' 
#' \deqn{
#'     y_{i} = e^{x_{i}} - 1
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_expm1
#'
#' @export
NULL


#' Eye
#'
#' @section eye(n, m=NULL, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
#'
#' Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.
#'
#'
#' @param n (int) the number of rows
#' @param m (int, optional) the number of columns with default being `n`
#' 
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#'
#' @name torch_eye
#'
#' @export
NULL


#' Flatten
#'
#' @section flatten(input, start_dim=0, end_dim=-1) -> Tensor :
#'
#' Flattens a contiguous range of dims in a tensor.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param start_dim (int) the first dim to flatten
#' @param end_dim (int) the last dim to flatten
#' @param dims if tensor is named you can pass the name of the dimensions to 
#'   flatten
#' @param out_dim the name of the resulting dimension if a named tensor.
#'
#' @name torch_flatten
#'
#' @export
NULL


#' Floor
#'
#' @section floor(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the floor of the elements of `input`,
#' the largest integer less than or equal to each element.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \left\lfloor \mbox{input}_{i} \right\rfloor
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_floor
#'
#' @export
NULL


#' Floor_divide
#'
#' @section floor_divide(input, other, out=NULL) -> Tensor :
#'
#' Return the division of the inputs rounded down to the nearest integer. See [`torch_div`]
#' for type promotion and broadcasting rules.
#' 
#' \deqn{
#'     \mbox{{out}}_i = \left\lfloor \frac{{\mbox{{input}}_i}}{{\mbox{{other}}_i}} \right\rfloor
#' }
#'
#'
#' @param self (Tensor) the numerator tensor
#' @param other (Tensor or Scalar) the denominator
#'
#' @name torch_floor_divide
#'
#' @export
NULL


#' Frac
#'
#' @section frac(input, out=NULL) -> Tensor :
#'
#' Computes the fractional portion of each element in `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \mbox{input}_{i} - \left\lfloor |\mbox{input}_{i}| \right\rfloor * \mbox{sgn}(\mbox{input}_{i})
#' }
#' 
#' @param self the input tensor.
#'
#'
#'
#'
#' @name torch_frac
#'
#' @export
NULL


#' Full
#'
#' @section full(size, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
#'
#' Returns a tensor of size `size` filled with `fill_value`.
#' 
#' @section Warning:
#'     In PyTorch 1.5 a bool or integral `fill_value` will produce a warning if
#'     `dtype` or `out` are not set.
#'     In a future PyTorch release, when `dtype` and `out` are not set
#'     a bool `fill_value` will return a tensor of torch.bool dtype,
#'     and an integral `fill_value` will return a tensor of torch.long dtype.
#'
#'
#' @param size (int...) a list, tuple, or `torch_Size` of integers defining the        shape of the output tensor.
#' @param fill_value NA the number to fill the output tensor with.
#' 
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#' @param names optional names of the dimensions
#'
#' @name torch_full
#'
#' @export
NULL


#' Full_like
#'
#' @section full_like(input, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, :
#'
#' memory_format=torch.preserve_format) -> Tensor
#' 
#' Returns a tensor with the same size as `input` filled with `fill_value`.
#' `torch_full_like(input, fill_value)` is equivalent to
#' `torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)`.
#'
#'
#' @param input (Tensor) the size of `input` will determine size of the output tensor.
#' @param fill_value the number to fill the output tensor with.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: `torch_preserve_format`.
#'
#' @name torch_full_like
#'
#' @export
NULL


#' Hann_window
#'
#' @section hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
#'
#' Hann window function.
#' 
#' \deqn{
#'     w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] =
#'             \sin^2 \left( \frac{\pi n}{N - 1} \right),
#' }
#' where \eqn{N} is the full window size.
#' 
#' The input `window_length` is a positive integer controlling the
#' returned window size. `periodic` flag determines whether the returned
#' window trims off the last duplicate value from the symmetric window and is
#' ready to be used as a periodic window with functions like
#' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in
#' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
#' `torch_hann_window(L, periodic=TRUE)` equal to
#' `torch_hann_window(L + 1, periodic=False)[:-1])`.
#' 
#' @note
#'     If `window_length` \eqn{=1}, the returned window contains a single value 1.
#'
#'
#' @param window_length (int) the size of returned window
#' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          `torch_strided` (dense layout) is supported.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#'
#' @name torch_hann_window
#'
#' @export
NULL


#' Hamming_window
#'
#' @section hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
#'
#' Hamming window function.
#' 
#' \deqn{
#'     w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right),
#' }
#' where \eqn{N} is the full window size.
#' 
#' The input `window_length` is a positive integer controlling the
#' returned window size. `periodic` flag determines whether the returned
#' window trims off the last duplicate value from the symmetric window and is
#' ready to be used as a periodic window with functions like
#' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in
#' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
#' `torch_hamming_window(L, periodic=TRUE)` equal to
#' `torch_hamming_window(L + 1, periodic=False)[:-1])`.
#' 
#' @note
#'     If `window_length` \eqn{=1}, the returned window contains a single value 1.
#' 
#' @note
#'     This is a generalized version of `torch_hann_window`.
#'
#'
#' @param window_length (int) the size of returned window
#' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.
#' @param alpha (float, optional) The coefficient \eqn{\alpha} in the equation above
#' @param beta (float, optional) The coefficient \eqn{\beta} in the equation above
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          `torch_strided` (dense layout) is supported.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#'
#' @name torch_hamming_window
#'
#' @export
NULL


#' Ger
#'
#' @section ger(input, vec2, out=NULL) -> Tensor :
#'
#' Outer product of `input` and `vec2`.
#' If `input` is a vector of size \eqn{n} and `vec2` is a vector of
#' size \eqn{m}, then `out` must be a matrix of size \eqn{(n \times m)}.
#' 
#' @note This function does not broadcast .
#'
#'
#' @param self (Tensor) 1-D input vector
#' @param vec2 (Tensor) 1-D input vector
#'
#' @name torch_ger
#'
#' @export
NULL


#' Fft
#'
#' Computes the one dimensional discrete Fourier transform of input.
#'
#' @note 
#' The Fourier domain representation of any real signal satisfies the Hermitian 
#' property: `X[i] = conj(X[-i]).` This function always returns both the positive 
#' and negative frequency terms even though, for real inputs, the negative 
#' frequencies are redundant. rfft() returns the more compact one-sided representation
#' where only the positive frequencies are returned.
#'
#' @param self (Tensor) the input tensor
#' @param n (int) Signal length. If given, the input will either be zero-padded 
#'   or trimmed to this length before computing the FFT.
#' @param dim (int, optional) The dimension along which to take the one dimensional FFT.
#' @param norm (str, optional) Normalization mode. For the forward transform, these 
#' correspond to:
#' * "forward" - normalize by 1/n
#' * "backward" - no normalization
#' * "ortho" - normalize by 1/sqrt(n) (making the FFT orthonormal)
#' Calling the backward transform (ifft()) with the same normalization mode will 
#' apply an overall normalization of 1/n between the two transforms. This is 
#' required to make IFFT the exact inverse.
#' Default is "backward" (no normalization).
#' 
#' @examples 
#' t <- torch_arange(start = 0, end = 3)
#' t
#' torch_fft_fft(t, norm = "backward")
#' 
#' @name torch_fft_fft
#'
#' @export
NULL


#' Ifft
#'
#' Computes the one dimensional inverse discrete Fourier transform of input.
#' 
#' @param self (Tensor) the input tensor
#' @param n (int, optional) – Signal length. If given, the input will either be 
#'  zero-padded or trimmed to this length before computing the IFFT.
#' @param dim (int, optional) – The dimension along which to take the one 
#'  dimensional IFFT.
#' @param norm (str, optional) – Normalization mode. For the backward transform, 
#'  these correspond to:
#'    * "forward" - no normalization
#'    * "backward" - normalize by 1/n
#'    * "ortho" - normalize by 1/sqrt(n) (making the IFFT orthonormal)
#'  Calling the forward transform with the same normalization mode will apply an 
#'  overall normalization of 1/n between the two transforms. This is required to 
#'  make ifft() the exact inverse.
#'  Default is "backward" (normalize by 1/n).
#'  
#' @examples
#' t <- torch_arange(start = 0, end = 3)
#' t
#' x <- torch_fft_fft(t, norm = "backward")
#' torch_fft_ifft(x)
#' 
#'
#' @name torch_fft_ifft
#'
#' @export
NULL


#' Rfft
#' 
#' Computes the one dimensional Fourier transform of real-valued input.
#' 
#' The FFT of a real signal is Hermitian-symmetric, `X[i] = conj(X[-i])` so the 
#' output contains only the positive frequencies below the Nyquist frequency. 
#' To compute the full output, use [torch_fft_fft()].
#'
#' @param self (Tensor)  the real input tensor
#' @param n (int) Signal length. If given, the input will either be zero-padded 
#'  or trimmed to this length before computing the real FFT.
#' @param dim (int, optional) – The dimension along which to take the one 
#'  dimensional real FFT.
#' @param norm norm (str, optional) – Normalization mode. For the forward 
#'  transform, these correspond to:
#'   * "forward" - normalize by 1/n
#'   * "backward" - no normalization
#'   * "ortho" - normalize by 1/sqrt(n) (making the FFT orthonormal)
#'  Calling the backward transform ([torch_fft_irfft()]) with the same 
#'  normalization mode will apply an overall normalization of 1/n between the 
#'  two transforms. This is required to make irfft() the exact inverse.
#'  Default is "backward" (no normalization).
#'  
#' @examples 
#' t <- torch_arange(start = 0, end = 3)
#' torch_fft_rfft(t)
#'
#' @name torch_fft_rfft
#'
#' @export
NULL


#' Irfft
#' 
#' Computes the inverse of [torch_fft_rfft()].
#' Input is interpreted as a one-sided Hermitian signal in the Fourier domain, 
#' as produced by [torch_fft_rfft()]. By the Hermitian property, the output will 
#' be real-valued.
#' 
#' @note
#' Some input frequencies must be real-valued to satisfy the Hermitian property. 
#' In these cases the imaginary component will be ignored. For example, any 
#' imaginary component in the zero-frequency term cannot be represented in a real 
#' output and so will always be ignored.
#' 
#' @note 
#' The correct interpretation of the Hermitian input depends on the length of the 
#' original data, as given by n. This is because each input shape could correspond 
#' to either an odd or even length signal. By default, the signal is assumed to be 
#' even length and odd signals will not round-trip properly. So, it is recommended 
#' to always pass the signal length n.
#' 
#' @param self (Tensor) the input tensor representing a half-Hermitian signal
#' @param n (int) Output signal length. This determines the length of the output 
#'  signal. If given, the input will either be zero-padded or trimmed to this 
#'  length before computing the real IFFT. Defaults to even output: `n=2*(input.size(dim) - 1)`.
#' @param dim (int, optional) – The dimension along which to take the one 
#'  dimensional real IFFT.
#' @param norm (str, optional) – Normalization mode. For the backward transform,
#'  these correspond to:
#'   * "forward" - no normalization
#'   * "backward" - normalize by 1/n
#'   * "ortho" - normalize by 1/sqrt(n) (making the real IFFT orthonormal)
#'  Calling the forward transform ([torch_fft_rfft()]) with the same normalization
#'  mode will apply an overall normalization of 1/n between the two transforms. 
#'  This is required to make irfft() the exact inverse.
#'  Default is "backward" (normalize by 1/n).
#' 
#' @examples 
#' t <- torch_arange(start = 0, end = 4)
#' x <- torch_fft_rfft(t)
#' torch_fft_irfft(x)
#' torch_fft_irfft(x, n = t$numel())
#' 
#' @name torch_fft_irfft
#'
#' @export
NULL

#' fftfreq
#'
#' Computes the discrete Fourier Transform sample frequencies for a signal of size `n`.
#'
#' @note 
#' By convention, [torch_fft_fft()] returns positive frequency terms first, followed by the negative
#' frequencies in reverse order, so that `f[-i]` for all `0 < i <= n/2`
#' gives the negative frequency terms. For an FFT of length `n` and with inputs spaced
#' in length unit `d`, the frequencies are:
#' `f = [0, 1, ..., (n - 1) // 2, -(n // 2), ..., -1] / (d * n)`
#' 
#' @note 
#' For even lengths, the Nyquist frequency at `f[n/2]` can be thought of as either negative
#' or positive. `fftfreq()` follows NumPy’s convention of taking it to be negative.
#'
#' @param n (integer) – the FFT length
#' @param d (float, optional) – the sampling length scale. The spacing between individual
#' samples of the FFT input. The default assumes unit spacing, dividing that result by the
#' actual spacing gives the result in physical frequency units.
#' @param dtype (default: [torch_get_default_dtype()]) the desired data type of returned tensor. 
#' @param layout (default: [torch_strided()]) the desired layout of returned tensor. 
#' @param device (default: `NULL`) the desired device of returned tensor.  Default:
#' If `NULL`, uses the current device for the default tensor type. 
#' @param requires_grad (default: `FALSE`)  If autograd should record operations on the returned tensor.
#' 
#' @examples 
#' torch_fft_fftfreq(5) # Nyquist frequency at f[3] is positive
#' torch_fft_fftfreq(4) # Nyquist frequency at f[3] is given as negative
#' 
#' @name torch_fft_fftfreq
#'
#' @export
NULL


#' Inverse
#'
#' @section inverse(input, out=NULL) -> Tensor :
#'
#' Takes the inverse of the square matrix `input`. `input` can be batches
#' of 2D square tensors, in which case this function would return a tensor composed of
#' individual inverses.
#' 
#' @note
#' 
#'     Irrespective of the original strides, the returned tensors will be
#'     transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()`
#'
#'
#' @param self (Tensor) the input tensor of size \eqn{(*, n, n)} where `*` is zero or more                    batch dimensions
#' 
#'
#' @name torch_inverse
#'
#' @export
NULL


#' Isnan
#'
#' @section TEST :
#'
#' Returns a new tensor with boolean elements representing if each element is `NaN` or not.
#'
#'
#' @param self (Tensor) A tensor to check
#'
#' @name torch_isnan
#'
#' @export
NULL


#' Is_floating_point
#'
#' @section is_floating_point(input) -> (bool) :
#'
#' Returns TRUE if the data type of `input` is a floating point data type i.e.,
#' one of `torch_float64`, `torch.float32` and `torch.float16`.
#'
#'
#' @param self (Tensor) the PyTorch tensor to test
#'
#' @name torch_is_floating_point
#'
#' @export
NULL


#' Is_complex
#'
#' @section is_complex(input) -> (bool) :
#'
#' Returns TRUE if the data type of `input` is a complex data type i.e.,
#' one of `torch_complex64`, and `torch.complex128`.
#'
#'
#' @param self (Tensor) the PyTorch tensor to test
#'
#' @name torch_is_complex
#'
#' @export
NULL


#' Kthvalue
#'
#' @section kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
#'
#' Returns a namedtuple `(values, indices)` where `values` is the `k` th
#' smallest element of each row of the `input` tensor in the given dimension
#' `dim`. And `indices` is the index location of each element found.
#' 
#' If `dim` is not given, the last dimension of the `input` is chosen.
#' 
#' If `keepdim` is `TRUE`, both the `values` and `indices` tensors
#' are the same size as `input`, except in the dimension `dim` where
#' they are of size 1. Otherwise, `dim` is squeezed
#' (see [`torch_squeeze`]), resulting in both the `values` and
#' `indices` tensors having 1 fewer dimension than the `input` tensor.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param k (int) k for the k-th smallest element
#' @param dim (int, optional) the dimension to find the kth value along
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#'
#' @name torch_kthvalue
#'
#' @export
NULL


#' Linspace
#'
#' @section linspace(start, end, steps=100, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
#'
#' Returns a one-dimensional tensor of `steps`
#' equally spaced points between `start` and `end`.
#' 
#' The output tensor is 1-D of size `steps`.
#'
#'
#' @param start (float) the starting value for the set of points
#' @param end (float) the ending value for the set of points
#' @param steps (int) number of points to sample between `start`        and `end`. Default: `100`.
#' 
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#'
#' @name torch_linspace
#'
#' @export
NULL


#' Log
#'
#' @section log(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the natural logarithm of the elements
#' of `input`.
#' 
#' \deqn{
#'     y_{i} = \log_{e} (x_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_log
#'
#' @export
NULL


#' Log10
#'
#' @section log10(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the logarithm to the base 10 of the elements
#' of `input`.
#' 
#' \deqn{
#'     y_{i} = \log_{10} (x_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_log10
#'
#' @export
NULL


#' Log1p
#'
#' @section log1p(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the natural logarithm of (1 + `input`).
#' 
#' \deqn{
#'     y_i = \log_{e} (x_i + 1)
#' }
#' @note This function is more accurate than [`torch_log`] for small
#'           values of `input`
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_log1p
#'
#' @export
NULL


#' Log2
#'
#' @section log2(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the logarithm to the base 2 of the elements
#' of `input`.
#' 
#' \deqn{
#'     y_{i} = \log_{2} (x_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_log2
#'
#' @export
NULL


#' Logdet
#'
#' @section logdet(input) -> Tensor :
#'
#' Calculates log determinant of a square matrix or batches of square matrices.
#' 
#' @note
#'     Result is `-inf` if `input` has zero log determinant, and is `NaN` if
#'     `input` has negative determinant.
#' 
#' @note
#'     Backward through `logdet` internally uses SVD results when `input`
#'     is not invertible. In this case, double backward through `logdet` will
#'     be unstable in when `input` doesn't have distinct singular values. See
#'     `~torch.svd` for details.
#'
#'
#' @param self (Tensor) the input tensor of size `(*, n, n)` where `*` is zero or more                batch dimensions.
#'
#' @name torch_logdet
#'
#' @export
NULL


#' Logspace
#'
#' @section logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
#'
#' Returns a one-dimensional tensor of `steps` points
#' logarithmically spaced with base `base` between
#' \eqn{{\mbox{base}}^{\mbox{start}}} and \eqn{{\mbox{base}}^{\mbox{end}}}.
#' 
#' The output tensor is 1-D of size `steps`.
#'
#'
#' @param start (float) the starting value for the set of points
#' @param end (float) the ending value for the set of points
#' @param steps (int) number of points to sample between `start`        and `end`. Default: `100`.
#' @param base (float) base of the logarithm function. Default: `10.0`.
#' 
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#'
#' @name torch_logspace
#'
#' @export
NULL


#' Logsumexp
#'
#' @section logsumexp(input, dim, keepdim=False, out=NULL) :
#'
#' Returns the log of summed exponentials of each row of the `input`
#' tensor in the given dimension `dim`. The computation is numerically
#' stabilized.
#' 
#' For summation index \eqn{j} given by `dim` and other indices \eqn{i}, the result is
#' 
#' \deqn{
#'         \mbox{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij})
#' }
#' 
#' If `keepdim` is `TRUE`, the output tensor is of the same size
#' as `input` except in the dimension(s) `dim` where it is of size 1.
#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#' 
#'
#' @name torch_logsumexp
#'
#' @export
NULL


#' Matmul
#'
#' @section matmul(input, other, out=NULL) -> Tensor :
#'
#' Matrix product of two tensors.
#' 
#' The behavior depends on the dimensionality of the tensors as follows:
#' 
#' - If both tensors are 1-dimensional, the dot product (scalar) is returned.
#' - If both arguments are 2-dimensional, the matrix-matrix product is returned.
#' - If the first argument is 1-dimensional and the second argument is 2-dimensional,
#'   a 1 is prepended to its dimension for the purpose of the matrix multiply.
#'   After the matrix multiply, the prepended dimension is removed.
#' - If the first argument is 2-dimensional and the second argument is 1-dimensional,
#'   the matrix-vector product is returned.
#' - If both arguments are at least 1-dimensional and at least one argument is
#'   N-dimensional (where N > 2), then a batched matrix multiply is returned.  If the first
#'   argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the
#'   batched matrix multiply and removed after.  If the second argument is 1-dimensional, a
#'   1 is appended to its dimension for the purpose of the batched matrix multiple and removed after.
#'   The non-matrix (i.e. batch) dimensions are broadcasted  (and thus
#'   must be broadcastable).  For example, if `input` is a
#'   \eqn{(j \times 1 \times n \times m)} tensor and `other` is a \eqn{(k \times m \times p)}
#'   tensor, `out` will be an \eqn{(j \times k \times n \times p)} tensor.
#' 
#' @note
#' 
#'     The 1-dimensional dot product version of this function does not support an `out` parameter.
#'
#'
#' @param self (Tensor) the first tensor to be multiplied
#' @param other (Tensor) the second tensor to be multiplied
#' 
#'
#' @name torch_matmul
#'
#' @export
NULL


#' Matrix_rank
#'
#' @section matrix_rank(input, tol=NULL, symmetric=False) -> Tensor :
#'
#' Returns the numerical rank of a 2-D tensor. The method to compute the
#' matrix rank is done using SVD by default. If `symmetric` is `TRUE`,
#' then `input` is assumed to be symmetric, and the computation of the
#' rank is done by obtaining the eigenvalues.
#' 
#' `tol` is the threshold below which the singular values (or the eigenvalues
#' when `symmetric` is `TRUE`) are considered to be 0. If `tol` is not
#' specified, `tol` is set to `S.max() * max(S.size()) * eps` where `S` is the
#' singular values (or the eigenvalues when `symmetric` is `TRUE`), and `eps`
#' is the epsilon value for the datatype of `input`.
#'
#'
#' @param self (Tensor) the input 2-D tensor
#' @param tol (float, optional) the tolerance value. Default: `NULL`
#' @param symmetric (bool, optional) indicates whether `input` is symmetric.                               Default: `FALSE`
#'
#' @name torch_matrix_rank
#'
NULL


#' Matrix_power
#'
#' @section matrix_power(input, n) -> Tensor :
#'
#' Returns the matrix raised to the power `n` for square matrices.
#' For batch of matrices, each individual matrix is raised to the power `n`.
#' 
#' If `n` is negative, then the inverse of the matrix (if invertible) is
#' raised to the power `n`.  For a batch of matrices, the batched inverse
#' (if invertible) is raised to the power `n`. If `n` is 0, then an identity matrix
#' is returned.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param n (int) the power to raise the matrix to
#'
#' @name torch_matrix_power
#'
#' @export
NULL


#' Max
#'
#' @section max(input) -> Tensor :
#'
#' Returns the maximum value of all elements in the `input` tensor.
#'
#' @section max(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
#'
#' Returns a namedtuple `(values, indices)` where `values` is the maximum
#' value of each row of the `input` tensor in the given dimension
#' `dim`. And `indices` is the index location of each maximum value found
#' (argmax).
#' 
#' @section Warning:
#'     `indices` does not necessarily contain the first occurrence of each
#'     maximal value found, unless it is unique.
#'     The exact implementation details are device-specific.
#'     Do not expect the same result when run on CPU and GPU in general.
#' 
#' If `keepdim` is `TRUE`, the output tensors are of the same size
#' as `input` except in the dimension `dim` where they are of size 1.
#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting
#' in the output tensors having 1 fewer dimension than `input`.
#'
#' @section max(input, other, out=NULL) -> Tensor :
#'
#' Each element of the tensor `input` is compared with the corresponding
#' element of the tensor `other` and an element-wise maximum is taken.
#' 
#' The shapes of `input` and `other` don't need to match,
#' but they must be broadcastable .
#' 
#' \deqn{
#'     \mbox{out}_i = \max(\mbox{tensor}_i, \mbox{other}_i)
#' }
#' @note When the shapes do not match, the shape of the returned output tensor
#'           follows the broadcasting rules .
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int) the dimension to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Default: `FALSE`.
#' @param out (tuple, optional) the result tuple of two output tensors (max, max_indices)
#' @param other (Tensor) the second input tensor
#'
#' @name torch_max
#'
#' @export
NULL


#' Mean
#'
#' @section mean(input) -> Tensor :
#'
#' Returns the mean value of all elements in the `input` tensor.
#'
#' @section mean(input, dim, keepdim=False, out=NULL) -> Tensor :
#'
#' Returns the mean value of each row of the `input` tensor in the given
#' dimension `dim`. If `dim` is a list of dimensions,
#' reduce over all of them.
#' 
#' 
#' If `keepdim` is `TRUE`, the output tensor is of the same size
#' as `input` except in the dimension(s) `dim` where it is of size 1.
#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#' @param dtype the resulting data type.
#'
#' @name torch_mean
#'
#' @export
NULL


#' Median
#'
#' @section median(input) -> Tensor :
#'
#' Returns the median value of all elements in the `input` tensor.
#'
#' @section median(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
#'
#' Returns a namedtuple `(values, indices)` where `values` is the median
#' value of each row of the `input` tensor in the given dimension
#' `dim`. And `indices` is the index location of each median value found.
#' 
#' By default, `dim` is the last dimension of the `input` tensor.
#' 
#' If `keepdim` is `TRUE`, the output tensors are of the same size
#' as `input` except in the dimension `dim` where they are of size 1.
#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in
#' the outputs tensor having 1 fewer dimension than `input`.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int) the dimension to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#'
#' @name torch_median
#'
#' @export
NULL


#' Min
#'
#' @section min(input) -> Tensor :
#'
#' Returns the minimum value of all elements in the `input` tensor.
#'
#' @section min(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
#'
#' Returns a namedtuple `(values, indices)` where `values` is the minimum
#' value of each row of the `input` tensor in the given dimension
#' `dim`. And `indices` is the index location of each minimum value found
#' (argmin).
#' 
#' @section Warning:
#'     `indices` does not necessarily contain the first occurrence of each
#'     minimal value found, unless it is unique.
#'     The exact implementation details are device-specific.
#'     Do not expect the same result when run on CPU and GPU in general.
#' 
#' If `keepdim` is `TRUE`, the output tensors are of the same size as
#' `input` except in the dimension `dim` where they are of size 1.
#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in
#' the output tensors having 1 fewer dimension than `input`.
#'
#' @section min(input, other, out=NULL) -> Tensor :
#'
#' Each element of the tensor `input` is compared with the corresponding
#' element of the tensor `other` and an element-wise minimum is taken.
#' The resulting tensor is returned.
#' 
#' The shapes of `input` and `other` don't need to match,
#' but they must be broadcastable .
#' 
#' \deqn{
#'     \mbox{out}_i = \min(\mbox{tensor}_i, \mbox{other}_i)
#' }
#' @note When the shapes do not match, the shape of the returned output tensor
#'           follows the broadcasting rules .
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int) the dimension to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#' @param out (tuple, optional) the tuple of two output tensors (min, min_indices)
#' @param other (Tensor) the second input tensor
#'
#' @name torch_min
#'
#' @export
NULL


#' Mm
#'
#' @section mm(input, mat2, out=NULL) -> Tensor :
#'
#' Performs a matrix multiplication of the matrices `input` and `mat2`.
#' 
#' If `input` is a \eqn{(n \times m)} tensor, `mat2` is a
#' \eqn{(m \times p)} tensor, `out` will be a \eqn{(n \times p)} tensor.
#' 
#' @note This function does not broadcast .
#'           For broadcasting matrix products, see [`torch_matmul`].
#'
#'
#' @param self (Tensor) the first matrix to be multiplied
#' @param mat2 (Tensor) the second matrix to be multiplied
#' 
#'
#' @name torch_mm
#'
#' @export
NULL


#' Mode
#'
#' @section mode(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
#'
#' Returns a namedtuple `(values, indices)` where `values` is the mode
#' value of each row of the `input` tensor in the given dimension
#' `dim`, i.e. a value which appears most often
#' in that row, and `indices` is the index location of each mode value found.
#' 
#' By default, `dim` is the last dimension of the `input` tensor.
#' 
#' If `keepdim` is `TRUE`, the output tensors are of the same size as
#' `input` except in the dimension `dim` where they are of size 1.
#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting
#' in the output tensors having 1 fewer dimension than `input`.
#' 
#' @note This function is not defined for `torch_cuda.Tensor` yet.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int) the dimension to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#'
#' @name torch_mode
#'
#' @export
NULL


#' Mul
#'
#' @section mul(input, other, out=NULL) :
#'
#' Multiplies each element of the input `input` with the scalar
#' `other` and returns a new resulting tensor.
#' 
#' \deqn{
#'     \mbox{out}_i = \mbox{other} \times \mbox{input}_i
#' }
#' If `input` is of type `FloatTensor` or `DoubleTensor`, `other`
#' should be a real number, otherwise it should be an integer
#'
#' @section mul(input, other, out=NULL) :
#'
#' Each element of the tensor `input` is multiplied by the corresponding
#' element of the Tensor `other`. The resulting tensor is returned.
#' 
#' The shapes of `input` and `other` must be
#' broadcastable .
#' 
#' \deqn{
#'     \mbox{out}_i = \mbox{input}_i \times \mbox{other}_i
#' }
#'
#' @param self (Tensor) the first multiplicand tensor
#' @param other (Tensor) the second multiplicand tensor
#' 
#'
#' @name torch_mul
#'
#' @export
NULL


#' Mv
#'
#' @section mv(input, vec, out=NULL) -> Tensor :
#'
#' Performs a matrix-vector product of the matrix `input` and the vector
#' `vec`.
#' 
#' If `input` is a \eqn{(n \times m)} tensor, `vec` is a 1-D tensor of
#' size \eqn{m}, `out` will be 1-D of size \eqn{n}.
#' 
#' @note This function does not broadcast .
#'
#'
#' @param self (Tensor) matrix to be multiplied
#' @param vec (Tensor) vector to be multiplied
#' 
#'
#' @name torch_mv
#'
#' @export
NULL


#' Mvlgamma
#'
#' @section mvlgamma(input, p) -> Tensor :
#'
#' Computes the `multivariate log-gamma function
#' <https://en.wikipedia.org/wiki/Multivariate_gamma_function>`_) with dimension
#' \eqn{p} element-wise, given by
#' 
#' \deqn{
#'     \log(\Gamma_{p}(a)) = C + \displaystyle \sum_{i=1}^{p} \log\left(\Gamma\left(a - \frac{i - 1}{2}\right)\right)
#' }
#' where \eqn{C = \log(\pi) \times \frac{p (p - 1)}{4}} and \eqn{\Gamma(\cdot)} is the Gamma function.
#' 
#' All elements must be greater than \eqn{\frac{p - 1}{2}}, otherwise an error would be thrown.
#'
#'
#' @param self (Tensor) the tensor to compute the multivariate log-gamma function
#' @param p (int) the number of dimensions
#'
#' @name torch_mvlgamma
#'
#' @export
NULL


#' Narrow
#'
#' @section narrow(input, dim, start, length) -> Tensor :
#'
#' Returns a new tensor that is a narrowed version of `input` tensor. The
#' dimension `dim` is input from `start` to `start + length`. The
#' returned tensor and `input` tensor share the same underlying storage.
#'
#'
#' @param self (Tensor) the tensor to narrow
#' @param dim (int) the dimension along which to narrow
#' @param start (int) the starting dimension
#' @param length (int) the distance to the ending dimension
#'
#' @name torch_narrow
#'
#' @export
NULL


#' Ones
#'
#' @section ones(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
#'
#' Returns a tensor filled with the scalar value `1`, with the shape defined
#' by the variable argument `size`.
#'
#'
#' @param ... (int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.
#' 
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#' @param names optional names for the dimensions
#'
#' @name torch_ones
#'
#' @export
NULL


#' Ones_like
#'
#' @section ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
#'
#' Returns a tensor filled with the scalar value `1`, with the same size as
#' `input`. `torch_ones_like(input)` is equivalent to
#' `torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`.
#' 
#' @section Warning:
#'     As of 0.4, this function does not support an `out` keyword. As an alternative,
#'     the old `torch_ones_like(input, out=output)` is equivalent to
#'     `torch_ones(input.size(), out=output)`.
#'
#'
#' @param input (Tensor) the size of `input` will determine size of the output tensor.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: `torch_preserve_format`.
#'
#' @name torch_ones_like
#'
#' @export
NULL


#' Cdist
#'
#' @section TEST :
#'
#' Computes batched the p-norm distance between each pair of the two collections of row vectors.
#'
#'
#' @param x1 (Tensor) input tensor of shape \eqn{B \times P \times M}.
#' @param x2 (Tensor) input tensor of shape \eqn{B \times R \times M}.
#' @param p NA p value for the p-norm distance to calculate between each vector pair        \eqn{\in [0, \infty]}.
#' @param compute_mode NA 'use_mm_for_euclid_dist_if_necessary' - will use matrix multiplication approach to calculate        euclidean distance (p = 2) if P > 25 or R > 25        'use_mm_for_euclid_dist' - will always use matrix multiplication approach to calculate        euclidean distance (p = 2)        'donot_use_mm_for_euclid_dist' - will never use matrix multiplication approach to calculate        euclidean distance (p = 2)        Default: use_mm_for_euclid_dist_if_necessary.
#'
#' @name torch_cdist
#'
#' @export
NULL


#' Pdist
#'
#' @section pdist(input, p=2) -> Tensor :
#'
#' Computes the p-norm distance between every pair of row vectors in the input.
#' This is identical to the upper triangular portion, excluding the diagonal, of
#' `torch_norm(input[:, NULL] - input, dim=2, p=p)`. This function will be faster
#' if the rows are contiguous.
#' 
#' If input has shape \eqn{N \times M} then the output will have shape
#' \eqn{\frac{1}{2} N (N - 1)}.
#' 
#' This function is equivalent to `scipy.spatial.distance.pdist(input,
#' 'minkowski', p=p)` if \eqn{p \in (0, \infty)}. When \eqn{p = 0} it is
#' equivalent to `scipy.spatial.distance.pdist(input, 'hamming') * M`.
#' When \eqn{p = \infty}, the closest scipy function is
#' `scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max())`.
#'
#'
#' @param self NA input tensor of shape \eqn{N \times M}.
#' @param p NA p value for the p-norm distance to calculate between each vector pair        \eqn{\in [0, \infty]}.
#'
#' @name torch_pdist
#'
#' @export
NULL


#' Cosine_similarity
#'
#' @section cosine_similarity(x1, x2, dim=1, eps=1e-8) -> Tensor :
#'
#' Returns cosine similarity between x1 and x2, computed along dim.
#' 
#' \deqn{
#'     \mbox{similarity} = \frac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
#' }
#'
#'
#' @param x1 (Tensor) First input.
#' @param x2 (Tensor) Second input (of size matching x1).
#' @param dim (int, optional) Dimension of vectors. Default: 1
#' @param eps (float, optional) Small value to avoid division by zero.        Default: 1e-8
#'
#' @name torch_cosine_similarity
#'
#' @export
NULL


#' Pixel_shuffle
#'
#' @section Rearranges elements in a tensor of shape :math:`(*, C \times r^2, H, W)` to a :
#'
#' Rearranges elements in a tensor of shape \eqn{(*, C \times r^2, H, W)} to a
#' tensor of shape \eqn{(*, C, H \times r, W \times r)}.
#' 
#' See `~torch.nn.PixelShuffle` for details.
#'
#'
#' @param self (Tensor) the input tensor
#' @param upscale_factor (int) factor to increase spatial resolution by
#'
#' @name torch_pixel_shuffle
#'
#' @export
NULL


#' Pinverse
#'
#' @section pinverse(input, rcond=1e-15) -> Tensor :
#'
#' Calculates the pseudo-inverse (also known as the Moore-Penrose inverse) of a 2D tensor.
#' Please look at `Moore-Penrose inverse`_ for more details
#' 
#' @note
#'     This method is implemented using the Singular Value Decomposition.
#' 
#' @note
#'     The pseudo-inverse is not necessarily a continuous function in the elements of the matrix `[1]`_.
#'     Therefore, derivatives are not always existent, and exist for a constant rank only `[2]`_.
#'     However, this method is backprop-able due to the implementation by using SVD results, and
#'     could be unstable. Double-backward will also be unstable due to the usage of SVD internally.
#'     See `~torch.svd` for more details.
#'
#'
#' @param self (Tensor) The input tensor of size \eqn{(*, m, n)} where \eqn{*} is zero or more batch dimensions
#' @param rcond (float) A floating point value to determine the cutoff for small singular values.                   Default: 1e-15
#'
#' @name torch_pinverse
#'
#' @export
NULL


#' Rand
#'
#' @section rand(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
#'
#' Returns a tensor filled with random numbers from a uniform distribution
#' on the interval \eqn{[0, 1)}
#' 
#' The shape of the tensor is defined by the variable argument `size`.
#'
#'
#' @param ... (int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.
#' 
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#' @param names optional dimension names
#'
#' @name torch_rand
#'
#' @export
NULL


#' Rand_like
#'
#' @section rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
#'
#' Returns a tensor with the same size as `input` that is filled with
#' random numbers from a uniform distribution on the interval \eqn{[0, 1)}.
#' `torch_rand_like(input)` is equivalent to
#' `torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`.
#'
#'
#' @param input (Tensor) the size of `input` will determine size of the output tensor.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: `torch_preserve_format`.
#'
#' @name torch_rand_like
#'
#' @export
NULL


#' Randint
#'
#' @section randint(low=0, high, size, *, generator=NULL, out=NULL, \ :
#'
#' dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor
#' 
#' Returns a tensor filled with random integers generated uniformly
#' between `low` (inclusive) and `high` (exclusive).
#' 
#' The shape of the tensor is defined by the variable argument `size`.
#' 
#' .. note:
#'     With the global dtype default (`torch_float32`), this function returns
#'     a tensor with dtype `torch_int64`.
#'
#'
#' @param low (int, optional) Lowest integer to be drawn from the distribution. Default: 0.
#' @param high (int) One above the highest integer to be drawn from the distribution.
#' @param size (tuple) a tuple defining the shape of the output tensor.
#' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling
#' 
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#' @param memory_format memory format for the resulting tensor.
#' 
#' @name torch_randint
#'
#' @export
NULL


#' Randint_like
#'
#' @section randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, :
#'
#' memory_format=torch.preserve_format) -> Tensor
#' 
#' Returns a tensor with the same shape as Tensor `input` filled with
#' random integers generated uniformly between `low` (inclusive) and
#' `high` (exclusive).
#' 
#' .. note:
#'     With the global dtype default (`torch_float32`), this function returns
#'     a tensor with dtype `torch_int64`.
#'
#'
#' @param input (Tensor) the size of `input` will determine size of the output tensor.
#' @param low (int, optional) Lowest integer to be drawn from the distribution. Default: 0.
#' @param high (int) One above the highest integer to be drawn from the distribution.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#'
#' @name torch_randint_like
#'
#' @export
NULL


#' Randn
#'
#' @section randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
#'
#' Returns a tensor filled with random numbers from a normal distribution
#' with mean `0` and variance `1` (also called the standard normal
#' distribution).
#' 
#' \deqn{
#'     \mbox{out}_{i} \sim \mathcal{N}(0, 1)
#' }
#' The shape of the tensor is defined by the variable argument `size`.
#'
#'
#' @param ... (int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.
#' 
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#' @param names optional names for the dimensions
#'
#' @name torch_randn
#'
#' @export
NULL


#' Randn_like
#'
#' @section randn_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
#'
#' Returns a tensor with the same size as `input` that is filled with
#' random numbers from a normal distribution with mean 0 and variance 1.
#' `torch_randn_like(input)` is equivalent to
#' `torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`.
#'
#'
#' @param input (Tensor) the size of `input` will determine size of the output tensor.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: `torch_preserve_format`.
#'
#' @name torch_randn_like
#'
#' @export
NULL


#' Randperm
#'
#' @section randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -> LongTensor :
#'
#' Returns a random permutation of integers from `0` to `n - 1`.
#'
#'
#' @param n (int) the upper bound (exclusive)
#' 
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: `torch_int64`.
#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#'
#' @name torch_randperm
#'
#' @export
NULL


#' Range
#'
#' @section range(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
#'
#' Returns a 1-D tensor of size \eqn{\left\lfloor \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rfloor + 1}
#' with values from `start` to `end` with step `step`. Step is
#' the gap between two values in the tensor.
#' 
#' \deqn{
#'     \mbox{out}_{i+1} = \mbox{out}_i + \mbox{step}.
#' }
#' @section Warning:
#'     This function is deprecated in favor of [`torch_arange`].
#'
#'
#' @param start (float) the starting value for the set of points. Default: `0`.
#' @param end (float) the ending value for the set of points
#' @param step (float) the gap between each pair of adjacent points. Default: `1`.
#' 
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). If `dtype` is not given, infer the data type from the other input        arguments. If any of `start`, `end`, or `stop` are floating-point, the        `dtype` is inferred to be the default dtype, see        `~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to        be `torch.int64`.
#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#'
#' @name torch_range
#'
#' @export
NULL


#' Reciprocal
#'
#' @section reciprocal(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the reciprocal of the elements of `input`
#' 
#' \deqn{
#'     \mbox{out}_{i} = \frac{1}{\mbox{input}_{i}}
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_reciprocal
#'
#' @export
NULL


#' Neg
#'
#' @section neg(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the negative of the elements of `input`.
#' 
#' \deqn{
#'     \mbox{out} = -1 \times \mbox{input}
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_neg
#'
#' @export
NULL


#' Repeat_interleave
#'
#' @section repeat_interleave(input, repeats, dim=NULL) -> Tensor :
#'
#' Repeat elements of a tensor.
#' 
#' @section Warning:
#' 
#'     This is different from `torch_Tensor.repeat` but similar to `numpy.repeat`.
#'
#' @section repeat_interleave(repeats) -> Tensor :
#'
#' If the `repeats` is `tensor([n1, n2, n3, ...])`, then the output will be
#' `tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...])` where `0` appears `n1` times,
#' `1` appears `n2` times, `2` appears `n3` times, etc.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param repeats (Tensor or int) The number of repetitions for each element.        repeats is broadcasted to fit the shape of the given axis.
#' @param dim (int, optional) The dimension along which to repeat values.        By default, use the flattened input array, and return a flat output        array.
#' @param output_size  (int, optional) – Total output size for the given axis 
#'  ( e.g. sum of repeats). If given, it will avoid stream syncronization needed 
#'  to calculate output shape of the tensor.
#'
#' @name torch_repeat_interleave
#'
#' @export
NULL


#' Reshape
#'
#' @section reshape(input, shape) -> Tensor :
#'
#' Returns a tensor with the same data and number of elements as `input`,
#' but with the specified shape. When possible, the returned tensor will be a view
#' of `input`. Otherwise, it will be a copy. Contiguous inputs and inputs
#' with compatible strides can be reshaped without copying, but you should not
#' depend on the copying vs. viewing behavior.
#' 
#' See `torch_Tensor.view` on when it is possible to return a view.
#' 
#' A single dimension may be -1, in which case it's inferred from the remaining
#' dimensions and the number of elements in `input`.
#'
#'
#' @param self (Tensor) the tensor to be reshaped
#' @param shape (tuple of ints) the new shape
#'
#' @name torch_reshape
#'
#' @export
NULL


#' Round
#'
#' @section round(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with each of the elements of `input` rounded
#' to the closest integer.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param decimals Number of decimal places to round to (default: 0). 
#'  If decimals is negative, it specifies the number of positions to 
#'  the left of the decimal point.
#' 
#' @name torch_round
#'
#' @export
NULL


#' Rrelu_
#'
#' @section rrelu_(input, lower=1./8, upper=1./3, training=False) -> Tensor :
#'
#' In-place version of `torch_rrelu`.
#' 
#' @param self the input tensor
#' @param generator random number generator
#' @inheritParams nnf_rrelu
#'
#'
#'
#'
#' @name torch_rrelu_
#'
#' @export
NULL

#' Relu
#'
#' @section relu(input) -> Tensor :
#' 
#' Computes the relu tranformation.
#' 
#' @param self the input tensor
#'
#' @name torch_relu
#'
#' @export
NULL

#' Relu_
#'
#' @section relu_(input) -> Tensor :
#'
#' In-place version of [torch_relu()].
#' 
#' @param self the input tensor
#'
#' @name torch_relu_
#'
#' @export
NULL


#' Rsqrt
#'
#' @section rsqrt(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the reciprocal of the square-root of each of
#' the elements of `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \frac{1}{\sqrt{\mbox{input}_{i}}}
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_rsqrt
#'
#' @export
NULL

#' Selu
#'
#' @section selu(input) -> Tensor :
#' 
#' Computes the selu transformation.
#'
#' @param self the input tensor
#'
#' @name torch_selu
#'
#' @export
#' 
NULL


#' Selu_
#'
#' @section selu_(input) -> Tensor :
#'
#' In-place version of [torch_selu()].
#' 
#' @param self the input tensor
#'
#'
#' @name torch_selu_
#'
#' @export
NULL

#' Celu
#'
#' @section celu(input, alpha=1.) -> Tensor :
#' 
#' See [nnf_celu()] for more info.
#' 
#' @param self the input tensor
#' @param alpha the alpha value for the CELU formulation. Default: 1.0
#' 
#' @name torch_celu
#'
#' @export
NULL


#' Celu_
#'
#' @section celu_(input, alpha=1.) -> Tensor :
#'
#' In-place version of [torch_celu()].
#' 
#' @param self the input tensor
#' @param alpha the alpha value for the CELU formulation. Default: 1.0
#' 
#' @name torch_celu_
#'
#' @export
NULL


#' Sigmoid
#'
#' @section sigmoid(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the sigmoid of the elements of `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \frac{1}{1 + e^{-\mbox{input}_{i}}}
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_sigmoid
#'
#' @export
NULL


#' Sin
#'
#' @section sin(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the sine of the elements of `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \sin(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_sin
#'
#' @export
NULL


#' Sinh
#'
#' @section sinh(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the hyperbolic sine of the elements of
#' `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \sinh(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_sinh
#'
#' @export
NULL


#' Slogdet
#'
#' @section slogdet(input) -> (Tensor, Tensor) :
#'
#' Calculates the sign and log absolute value of the determinant(s) of a square matrix or batches of square matrices.
#' 
#' @note
#'     If `input` has zero determinant, this returns `(0, -inf)`.
#' 
#' @note
#'     Backward through `slogdet` internally uses SVD results when `input`
#'     is not invertible. In this case, double backward through `slogdet`
#'     will be unstable in when `input` doesn't have distinct singular values.
#'     See `~torch.svd` for details.
#'
#'
#' @param self (Tensor) the input tensor of size `(*, n, n)` where `*` is zero or more                batch dimensions.
#'
#' @name torch_slogdet
#'
#' @export
NULL


#' Split
#'
#' Splits the tensor into chunks. Each chunk is a view of the original tensor.
#' 
#' If `split_size` is an integer type, then `tensor` will
#' be split into equally sized chunks (if possible). Last chunk will be smaller if
#' the tensor size along the given dimension `dim` is not divisible by
#' `split_size`.
#' 
#' If `split_size` is a list, then `tensor` will be split
#' into `length(split_size)` chunks with sizes in `dim` according
#' to `split_size_or_sections`.
#'
#' @param self (Tensor) tensor to split.
#' @param split_size (int) size of a single chunk or 
#'   list of sizes for each chunk
#' @param dim (int) dimension along which to split the tensor.
#'
#' @name torch_split
#'
#' @export
NULL


#' Squeeze
#'
#' @section squeeze(input, dim=NULL, out=NULL) -> Tensor :
#'
#' Returns a tensor with all the dimensions of `input` of size `1` removed.
#' 
#' For example, if `input` is of shape:
#' \eqn{(A \times 1 \times B \times C \times 1 \times D)} then the `out` tensor
#' will be of shape: \eqn{(A \times B \times C \times D)}.
#' 
#' When `dim` is given, a squeeze operation is done only in the given
#' dimension. If `input` is of shape: \eqn{(A \times 1 \times B)},
#' `squeeze(input, 0)` leaves the tensor unchanged, but `squeeze(input, 1)`
#' will squeeze the tensor to the shape \eqn{(A \times B)}.
#' 
#' @note The returned tensor shares the storage with the input tensor,
#'           so changing the contents of one will change the contents of the other.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int, optional) if given, the input will be squeezed only in           this dimension
#' 
#'
#' @name torch_squeeze
#'
#' @export
NULL


#' Stack
#'
#' @section stack(tensors, dim=0, out=NULL) -> Tensor :
#'
#' Concatenates sequence of tensors along a new dimension.
#' 
#' All tensors need to be of the same size.
#'
#'
#' @param tensors (sequence of Tensors) sequence of tensors to concatenate
#' @param dim (int) dimension to insert. Has to be between 0 and the number        of dimensions of concatenated tensors (inclusive)
#' 
#'
#' @name torch_stack
#'
#' @export
NULL


#' Stft
#'
#' @section Short-time Fourier transform (STFT). :
#'
#' Short-time Fourier transform (STFT).
#' 
#'     Ignoring the optional batch dimension, this method computes the following
#'     expression:
#' 
#' \deqn{
#'         X[m, \omega] = \sum_{k = 0}^{\mbox{win\_length-1}}%
#'                             \mbox{window}[k]\ \mbox{input}[m \times \mbox{hop\_length} + k]\ %
#'                             \exp\left(- j \frac{2 \pi \cdot \omega k}{\mbox{win\_length}}\right),
#' }
#'     where \eqn{m} is the index of the sliding window, and \eqn{\omega} is
#'     the frequency that \eqn{0 \leq \omega < \mbox{n\_fft}}. When
#'     `onesided` is the default value `TRUE`,
#' 
#'     * `input` must be either a 1-D time sequence or a 2-D batch of time
#'       sequences.
#' 
#'     * If `hop_length` is `NULL` (default), it is treated as equal to
#'       `floor(n_fft / 4)`.
#' 
#'     * If `win_length` is `NULL` (default), it is treated as equal to
#'       `n_fft`.
#' 
#'     * `window` can be a 1-D tensor of size `win_length`, e.g., from
#'       `torch_hann_window`. If `window` is `NULL` (default), it is
#'       treated as if having \eqn{1} everywhere in the window. If
#'       \eqn{\mbox{win\_length} < \mbox{n\_fft}}, `window` will be padded on
#'       both sides to length `n_fft` before being applied.
#' 
#'     * If `center` is `TRUE` (default), `input` will be padded on
#'       both sides so that the \eqn{t}-th frame is centered at time
#'       \eqn{t \times \mbox{hop\_length}}. Otherwise, the \eqn{t}-th frame
#'       begins at time  \eqn{t \times \mbox{hop\_length}}.
#' 
#'     * `pad_mode` determines the padding method used on `input` when
#'       `center` is `TRUE`. See `torch_nn.functional.pad` for
#'       all available options. Default is `"reflect"`.
#' 
#'     * If `onesided` is `TRUE` (default), only values for \eqn{\omega}
#'       in \eqn{\left[0, 1, 2, \dots, \left\lfloor \frac{\mbox{n\_fft}}{2} \right\rfloor + 1\right]}
#'       are returned because the real-to-complex Fourier transform satisfies the
#'       conjugate symmetry, i.e., \eqn{X[m, \omega] = X[m, \mbox{n\_fft} - \omega]^*}.
#' 
#'     * If `normalized` is `TRUE` (default is `FALSE`), the function
#'       returns the normalized STFT results, i.e., multiplied by \eqn{(\mbox{frame\_length})^{-0.5}}.
#' 
#'     Returns the real and the imaginary parts together as one tensor of size
#'     \eqn{(* \times N \times T \times 2)}, where \eqn{*} is the optional
#'     batch size of `input`, \eqn{N} is the number of frequencies where
#'     STFT is applied, \eqn{T} is the total number of frames used, and each pair
#'     in the last dimension represents a complex number as the real part and the
#'     imaginary part.
#' 
#' @section Warning:
#' This function changed signature at version 0.4.1. Calling with the
#' previous signature may cause error or return incorrect result.
#'
#'
#' @param input (Tensor) the input tensor
#' @param n_fft (int) size of Fourier transform
#' @param hop_length (int, optional) the distance between neighboring sliding window        
#'   frames. Default: `NULL` (treated as equal to `floor(n_fft / 4)`)
#' @param win_length (int, optional) the size of window frame and STFT filter.
#'   Default: `NULL`  (treated as equal to `n_fft`)
#' @param window (Tensor, optional) the optional window function.        
#'   Default: `NULL` (treated as window of all \eqn{1} s)
#' @param center (bool, optional) whether to pad `input` on both sides so        
#'   that the \eqn{t}-th frame is centered at time \eqn{t \times \mbox{hop\_length}}.      
#'   Default: `TRUE`
#' @param pad_mode (string, optional) controls the padding method used when       
#'  `center` is `TRUE`. Default: `"reflect"`
#' @param normalized (bool, optional) controls whether to return the normalized 
#'   STFT results Default: `FALSE`
#' @param onesided (bool, optional) controls whether to return half of results to       
#'   avoid redundancy Default: `TRUE`
#' @param return_complex (bool, optional) controls whether to return complex tensors
#'   or not.
#' @name torch_stft
#'
#' @export
NULL


#' Sum
#'
#' @section sum(input, dtype=NULL) -> Tensor :
#'
#' Returns the sum of all elements in the `input` tensor.
#'
#' @section sum(input, dim, keepdim=False, dtype=NULL) -> Tensor :
#'
#' Returns the sum of each row of the `input` tensor in the given
#' dimension `dim`. If `dim` is a list of dimensions,
#' reduce over all of them.
#' 
#' 
#' If `keepdim` is `TRUE`, the output tensor is of the same size
#' as `input` except in the dimension(s) `dim` where it is of size 1.
#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to `dtype` before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.
#' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#'
#' @name torch_sum
#'
#' @export
NULL


#' Sqrt
#'
#' @section sqrt(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the square-root of the elements of `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \sqrt{\mbox{input}_{i}}
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_sqrt
#'
#' @export
NULL


#' Square
#'
#' @section square(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the square of the elements of `input`.
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_square
#'
#' @export
NULL


#' Std
#'
#' @section std(input, unbiased=TRUE) -> Tensor :
#'
#' Returns the standard-deviation of all elements in the `input` tensor.
#' 
#' If `unbiased` is `FALSE`, then the standard-deviation will be calculated
#' via the biased estimator. Otherwise, Bessel's correction will be used.
#'
#' @section std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -> Tensor :
#'
#' Returns the standard-deviation of each row of the `input` tensor in the
#' dimension `dim`. If `dim` is a list of dimensions,
#' reduce over all of them.
#' 
#' 
#' If `keepdim` is `TRUE`, the output tensor is of the same size
#' as `input` except in the dimension(s) `dim` where it is of size 1.
#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
#' 
#' 
#' If `unbiased` is `FALSE`, then the standard-deviation will be calculated
#' via the biased estimator. Otherwise, Bessel's correction will be used.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param unbiased (bool) whether to use the unbiased estimation or not
#' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#' @param correction The type of correction.
#'
#' @name torch_std
#'
#' @export
NULL


#' Std_mean
#'
#' @section std_mean(input, unbiased=TRUE) -> (Tensor, Tensor) :
#'
#' Returns the standard-deviation and mean of all elements in the `input` tensor.
#' 
#' If `unbiased` is `FALSE`, then the standard-deviation will be calculated
#' via the biased estimator. Otherwise, Bessel's correction will be used.
#'
#' @section std_mean(input, dim, unbiased=TRUE, keepdim=False) -> (Tensor, Tensor) :
#'
#' Returns the standard-deviation and mean of each row of the `input` tensor in the
#' dimension `dim`. If `dim` is a list of dimensions,
#' reduce over all of them.
#' 
#' 
#' If `keepdim` is `TRUE`, the output tensor is of the same size
#' as `input` except in the dimension(s) `dim` where it is of size 1.
#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
#' 
#' 
#' If `unbiased` is `FALSE`, then the standard-deviation will be calculated
#' via the biased estimator. Otherwise, Bessel's correction will be used.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param unbiased (bool) whether to use the unbiased estimation or not
#' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#' @inheritParams torch_std
#'
#' @name torch_std_mean
#'
#' @export
NULL


#' Prod
#'
#' @section prod(input, dtype=NULL) -> Tensor :
#'
#' Returns the product of all elements in the `input` tensor.
#'
#' @section prod(input, dim, keepdim=False, dtype=NULL) -> Tensor :
#'
#' Returns the product of each row of the `input` tensor in the given
#' dimension `dim`.
#' 
#' If `keepdim` is `TRUE`, the output tensor is of the same size
#' as `input` except in the dimension `dim` where it is of size 1.
#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in
#' the output tensor having 1 fewer dimension than `input`.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to `dtype` before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.
#' @param dim (int) the dimension to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#'
#' @name torch_prod
#'
#' @export
NULL


#' T
#'
#' @section t(input) -> Tensor :
#'
#' Expects `input` to be <= 2-D tensor and transposes dimensions 0
#' and 1.
#' 
#' 0-D and 1-D tensors are returned as is. When input is a 2-D tensor this
#' is equivalent to `transpose(input, 0, 1)`.
#'
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_t
#'
#' @export
NULL


#' Tan
#'
#' @section tan(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the tangent of the elements of `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \tan(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_tan
#'
#' @export
NULL


#' Tanh
#'
#' @section tanh(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the hyperbolic tangent of the elements
#' of `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \tanh(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_tanh
#'
#' @export
NULL


#' Tensordot
#'
#' Returns a contraction of a and b over multiple dimensions.
#' `tensordot` implements a generalized matrix product.
#'
#'
#' @param a (Tensor) Left tensor to contract
#' @param b (Tensor) Right tensor to contract
#' @param dims (int or tuple of two lists of integers) number of dimensions to     contract or explicit lists of dimensions for `a` and     `b` respectively
#'
#' @name torch_tensordot
#'
#' @export
NULL


#' Threshold_
#'
#' @section threshold_(input, threshold, value) -> Tensor :
#'
#' In-place version of `torch_threshold`.
#'
#' @param self input tensor
#' @param threshold The value to threshold at
#' @param value The value to replace with
#'
#' @name torch_threshold_
#'
#' @export
NULL


#' Transpose
#'
#' @section transpose(input, dim0, dim1) -> Tensor :
#'
#' Returns a tensor that is a transposed version of `input`.
#' The given dimensions `dim0` and `dim1` are swapped.
#' 
#' The resulting `out` tensor shares it's underlying storage with the
#' `input` tensor, so changing the content of one would change the content
#' of the other.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim0 (int) the first dimension to be transposed
#' @param dim1 (int) the second dimension to be transposed
#'
#' @name torch_transpose
#'
#' @export
NULL


#' Flip
#'
#' @section flip(input, dims) -> Tensor :
#'
#' Reverse the order of a n-D tensor along given axis in dims.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dims (a list or tuple) axis to flip on
#'
#' @name torch_flip
#'
#' @export
NULL


#' Roll
#'
#' @section roll(input, shifts, dims=NULL) -> Tensor :
#'
#' Roll the tensor along the given dimension(s). Elements that are shifted beyond the
#' last position are re-introduced at the first position. If a dimension is not
#' specified, the tensor will be flattened before rolling and then restored
#' to the original shape.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param shifts (int or tuple of ints) The number of places by which the elements        of the tensor are shifted. If shifts is a tuple, dims must be a tuple of        the same size, and each dimension will be rolled by the corresponding        value
#' @param dims (int or tuple of ints) Axis along which to roll
#'
#' @name torch_roll
#'
#' @export
NULL


#' Rot90
#'
#' @section rot90(input, k, dims) -> Tensor :
#'
#' Rotate a n-D tensor by 90 degrees in the plane specified by dims axis.
#' Rotation direction is from the first towards the second axis if k > 0, and from the second towards the first for k < 0.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param k (int) number of times to rotate
#' @param dims (a list or tuple) axis to rotate
#'
#' @name torch_rot90
#'
#' @export
NULL


#' Trapz
#'
#' @section trapz(y, x, *, dim=-1) -> Tensor :
#'
#' Estimate \eqn{\int y\,dx} along `dim`, using the trapezoid rule.
#'
#' @section trapz(y, *, dx=1, dim=-1) -> Tensor :
#'
#' As above, but the sample points are spaced uniformly at a distance of `dx`.
#'
#'
#' @param y (Tensor) The values of the function to integrate
#' @param x (Tensor) The points at which the function `y` is sampled.        If `x` is not in ascending order, intervals on which it is decreasing        contribute negatively to the estimated integral (i.e., the convention        \eqn{\int_a^b f = -\int_b^a f} is followed).
#' @param dim (int) The dimension along which to integrate.        By default, use the last dimension.
#' @param dx (float) The distance between points at which `y` is sampled.
#'
#' @name torch_trapz
#'
#' @export
NULL


#' TRUE_divide
#'
#' @section true_divide(dividend, divisor) -> Tensor :
#'
#' Performs "true division" that always computes the division
#' in floating point. Analogous to division in Python 3 and equivalent to
#' [`torch_div`] except when both inputs have bool or integer scalar types,
#' in which case they are cast to the default (floating) scalar type before the division.
#' 
#' \deqn{
#'     \mbox{out}_i = \frac{\mbox{dividend}_i}{\mbox{divisor}}
#' }
#'
#'
#' @param self (Tensor) the dividend
#' @param other (Tensor or Scalar) the divisor
#'
#' @name torch_true_divide
#'
#' @export
NULL


#' Trunc
#'
#' @section trunc(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the truncated integer values of
#' the elements of `input`.
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_trunc
#'
#' @export
NULL


#' Unique_consecutive
#'
#' @section TEST :
#'
#' Eliminates all but the first element from every consecutive group of equivalent elements.
#' 
#'     .. note:: This function is different from [`torch_unique`] in the sense that this function
#'         only eliminates consecutive duplicate values. This semantics is similar to `std::unique`
#'         in C++.
#'
#'
#' @param self (Tensor) the input tensor
#' @param return_inverse (bool) Whether to also return the indices for where        elements in the original input ended up in the returned unique list.
#' @param return_counts (bool) Whether to also return the counts for each unique        element.
#' @param dim (int) the dimension to apply unique. If `NULL`, the unique of the        flattened input is returned. default: `NULL`
#'
#' @name torch_unique_consecutive
#'
#' @export
NULL


#' Unsqueeze
#'
#' @section unsqueeze(input, dim) -> Tensor :
#'
#' Returns a new tensor with a dimension of size one inserted at the
#' specified position.
#' 
#' The returned tensor shares the same underlying data with this tensor.
#' 
#' A `dim` value within the range `[-input.dim() - 1, input.dim() + 1)`
#' can be used. Negative `dim` will correspond to `unsqueeze`
#' applied at `dim` = `dim + input.dim() + 1`.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int) the index at which to insert the singleton dimension
#'
#' @name torch_unsqueeze
#'
#' @export
NULL


#' Var
#'
#' @section var(input, unbiased=TRUE) -> Tensor :
#'
#' Returns the variance of all elements in the `input` tensor.
#' 
#' If `unbiased` is `FALSE`, then the variance will be calculated via the
#' biased estimator. Otherwise, Bessel's correction will be used.
#'
#' @section var(input, dim, keepdim=False, unbiased=TRUE, out=NULL) -> Tensor :
#'
#' Returns the variance of each row of the `input` tensor in the given
#' dimension `dim`.
#' 
#' 
#' If `keepdim` is `TRUE`, the output tensor is of the same size
#' as `input` except in the dimension(s) `dim` where it is of size 1.
#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
#' 
#' 
#' If `unbiased` is `FALSE`, then the variance will be calculated via the
#' biased estimator. Otherwise, Bessel's correction will be used.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param unbiased (bool) whether to use the unbiased estimation or not
#' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#' @inheritParams torch_std
#'
#' @name torch_var
#'
#' @export
NULL


#' Var_mean
#'
#' @section var_mean(input, unbiased=TRUE) -> (Tensor, Tensor) :
#'
#' Returns the variance and mean of all elements in the `input` tensor.
#' 
#' If `unbiased` is `FALSE`, then the variance will be calculated via the
#' biased estimator. Otherwise, Bessel's correction will be used.
#'
#' @section var_mean(input, dim, keepdim=False, unbiased=TRUE) -> (Tensor, Tensor) :
#'
#' Returns the variance and mean of each row of the `input` tensor in the given
#' dimension `dim`.
#' 
#' 
#' If `keepdim` is `TRUE`, the output tensor is of the same size
#' as `input` except in the dimension(s) `dim` where it is of size 1.
#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
#' 
#' 
#' If `unbiased` is `FALSE`, then the variance will be calculated via the
#' biased estimator. Otherwise, Bessel's correction will be used.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param unbiased (bool) whether to use the unbiased estimation or not
#' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#' @inheritParams torch_std
#'
#' @name torch_var_mean
#'
#' @export
NULL


#' Where
#'
#' @section where(condition, x, y) -> Tensor :
#'
#' Return a tensor of elements selected from either `x` or `y`, depending on `condition`.
#' 
#' The operation is defined as:
#' 
#' \deqn{
#'     \mbox{out}_i = \left\{ \begin{array}{ll}
#'         \mbox{x}_i & \mbox{if } \mbox{condition}_i \\
#'         \mbox{y}_i & \mbox{otherwise} \\
#'     \end{array}
#'     \right.
#' }
#' @note
#'     The tensors `condition`, `x`, `y` must be broadcastable .
#'
#' @section where(condition) -> tuple of LongTensor :
#'
#' `torch_where(condition)` is identical to
#' `torch_nonzero(condition, as_tuple=TRUE)`.
#' 
#' @note
#' See also [torch_nonzero()].
#'
#'
#' @param condition (BoolTensor) When TRUE (nonzero), yield x, otherwise yield y
#' @param self (Tensor) values selected at indices where `condition` is `TRUE`
#' @param other (Tensor) values selected at indices where `condition` is `FALSE`
#'
#' @name torch_where
#'
#' @export
NULL


#' Zeros
#'
#' @section zeros(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
#'
#' Returns a tensor filled with the scalar value `0`, with the shape defined
#' by the variable argument `size`.
#'
#'
#' @param ... a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.
#' 
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#' @param names optional dimension names
#' 
#' @name torch_zeros
#'
#' @export
NULL


#' Zeros_like
#'
#' @section zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
#'
#' Returns a tensor filled with the scalar value `0`, with the same size as
#' `input`. `torch_zeros_like(input)` is equivalent to
#' `torch_zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`.
#' 
#' @section Warning:
#'     As of 0.4, this function does not support an `out` keyword. As an alternative,
#'     the old `torch_zeros_like(input, out=output)` is equivalent to
#'     `torch_zeros(input.size(), out=output)`.
#'
#'
#' @param input (Tensor) the size of `input` will determine size of the output tensor.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: `torch_preserve_format`.
#'
#' @name torch_zeros_like
#'
#' @export
NULL


#' Poisson
#'
#' @section poisson(input *, generator=NULL) -> Tensor :
#'
#' Returns a tensor of the same size as `input` with each element
#' sampled from a Poisson distribution with rate parameter given by the corresponding
#' element in `input` i.e.,
#' 
#' \deqn{
#'     \mbox{out}_i \sim \mbox{Poisson}(\mbox{input}_i)
#' }
#'
#'
#' @param self (Tensor) the input tensor containing the rates of the Poisson distribution
#' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling
#'
#' @name torch_poisson
#'
#' @export
NULL


#' Norm
#'
#' @section TEST :
#'
#' Returns the matrix norm or vector norm of a given tensor.
#'
#'
#' @param self (Tensor) the input tensor
#' @param p (int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: `'fro'`        The following norms can be calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        NULL   Frobenius norm                2-norm        'fro'  Frobenius norm                --        'nuc'  nuclear norm                  --        Other  as vec norm when dim is NULL  sum(abs(x)**ord)**(1./ord)        =====  ============================  ==========================
#' @param dim (int, 2-tuple of ints, 2-list of ints, optional) If it is an int,        vector norm will be calculated, if it is 2-tuple of ints, matrix norm        will be calculated. If the value is NULL, matrix norm will be calculated        when the input tensor only has two dimensions, vector norm will be        calculated when the input tensor only has one dimension. If the input        tensor has more than two dimensions, the vector norm will be applied to        last dimension.
#' @param keepdim (bool, optional) whether the output tensors have `dim`        retained or not. Ignored if `dim` = `NULL` and        `out` = `NULL`. Default: `FALSE`
#'  Ignored if        `dim` = `NULL` and `out` = `NULL`.
#' @param dtype (`torch.dtype`, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: NULL.
#'
#' @name torch_norm
#'
#' @export
NULL


#' Pow
#'
#' @section pow(input, exponent, out=NULL) -> Tensor :
#'
#' Takes the power of each element in `input` with `exponent` and
#' returns a tensor with the result.
#' 
#' `exponent` can be either a single `float` number or a `Tensor`
#' with the same number of elements as `input`.
#' 
#' When `exponent` is a scalar value, the operation applied is:
#' 
#' \deqn{
#'     \mbox{out}_i = x_i^{\mbox{exponent}}
#' }
#' When `exponent` is a tensor, the operation applied is:
#' 
#' \deqn{
#'     \mbox{out}_i = x_i^{\mbox{exponent}_i}
#' }
#' When `exponent` is a tensor, the shapes of `input`
#' and `exponent` must be broadcastable .
#'
#' @section pow(self, exponent, out=NULL) -> Tensor :
#'
#' `self` is a scalar `float` value, and `exponent` is a tensor.
#' The returned tensor `out` is of the same shape as `exponent`
#' 
#' The operation applied is:
#' 
#' \deqn{
#'     \mbox{out}_i = \mbox{self} ^ {\mbox{exponent}_i}
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' @param exponent (float or tensor) the exponent value
#' 
#' @param self (float) the scalar base value for the power operation
#'
#' @name torch_pow
#'
#' @export
NULL


#' Addmm
#'
#' @section addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -> Tensor :
#'
#' Performs a matrix multiplication of the matrices `mat1` and `mat2`.
#' The matrix `input` is added to the final result.
#' 
#' If `mat1` is a \eqn{(n \times m)} tensor, `mat2` is a
#' \eqn{(m \times p)} tensor, then `input` must be
#' broadcastable  with a \eqn{(n \times p)} tensor
#' and `out` will be a \eqn{(n \times p)} tensor.
#' 
#' `alpha` and `beta` are scaling factors on matrix-vector product between
#' `mat1` and `mat2` and the added matrix `input` respectively.
#' 
#' \deqn{
#'     \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{mat1}_i \mathbin{@} \mbox{mat2}_i)
#' }
#' For inputs of type `FloatTensor` or `DoubleTensor`, arguments `beta` and
#' `alpha` must be real numbers, otherwise they should be integers.
#'
#'
#' @param self (Tensor) matrix to be added
#' @param mat1 (Tensor) the first matrix to be multiplied
#' @param mat2 (Tensor) the second matrix to be multiplied
#' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
#' @param alpha (Number, optional) multiplier for \eqn{mat1 @ mat2} (\eqn{\alpha})
#' 
#'
#' @name torch_addmm
#'
#' @export
NULL


#' Sparse_coo_tensor
#'
#' @section sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -> Tensor :
#'
#' Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given `indices`
#' with the given `values`. A sparse tensor can be `uncoalesced`, in that case, there are duplicate
#' coordinates in the indices, and the value at that index is the sum of all duplicate value entries:
#' `torch_sparse`_.
#'
#'
#' @param indices (array_like) Initial data for the tensor. Can be a list, tuple,        NumPy `ndarray`, scalar, and other types. Will be cast to a `torch_LongTensor`        internally. The indices are the coordinates of the non-zero values in the matrix, and thus        should be two-dimensional where the first dimension is the number of tensor dimensions and        the second dimension is the number of non-zero values.
#' @param values (array_like) Initial values for the tensor. Can be a list, tuple,        NumPy `ndarray`, scalar, and other types.
#' @param size (list, tuple, or `torch.Size`, optional) Size of the sparse tensor. If not        provided the size will be inferred as the minimum size big enough to hold all non-zero        elements.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if NULL, infers data type from `values`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if NULL, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
#'
#' @name torch_sparse_coo_tensor
#'
#' @export
NULL


#' Unbind
#'
#' @section unbind(input, dim=0) -> seq :
#'
#' Removes a tensor dimension.
#' 
#' Returns a tuple of all slices along a given dimension, already without it.
#'
#'
#' @param self (Tensor) the tensor to unbind
#' @param dim (int) dimension to remove
#'
#' @name torch_unbind
#'
#' @export
NULL


#' Quantize_per_tensor
#'
#' @section quantize_per_tensor(input, scale, zero_point, dtype) -> Tensor :
#'
#' Converts a float tensor to quantized tensor with given scale and zero point.
#'
#'
#' @param self (Tensor) float tensor to quantize
#' @param scale (float) scale to apply in quantization formula
#' @param zero_point (int) offset in integer value that maps to float zero
#' @param dtype (`torch.dtype`) the desired data type of returned tensor.        Has to be one of the quantized dtypes: `torch_quint8`, `torch.qint8`, `torch.qint32`
#'
#' @name torch_quantize_per_tensor
#'
#' @export
NULL


#' Quantize_per_channel
#'
#' @section quantize_per_channel(input, scales, zero_points, axis, dtype) -> Tensor :
#'
#' Converts a float tensor to per-channel quantized tensor with given scales and zero points.
#'
#'
#' @param self (Tensor) float tensor to quantize
#' @param scales (Tensor) float 1D tensor of scales to use, size should match `input.size(axis)`
#' @param zero_points (int) integer 1D tensor of offset to use, size should match `input.size(axis)`
#' @param axis (int) dimension on which apply per-channel quantization
#' @param dtype (`torch.dtype`) the desired data type of returned tensor.        Has to be one of the quantized dtypes: `torch_quint8`, `torch.qint8`, `torch.qint32`
#'
#' @name torch_quantize_per_channel
#'
#' @export
NULL


#' Meshgrid
#'
#' Take \eqn{N} tensors, each of which can be either scalar or 1-dimensional
#' vector, and create \eqn{N} N-dimensional grids, where the \eqn{i} `th` grid is defined by
#' expanding the \eqn{i} `th` input over dimensions defined by other inputs.
#'
#'
#' @param tensors (list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be
#'  treated (1,).
#' @param indexing (str, optional): the indexing mode, either “xy” or “ij”, defaults to “ij”. 
#'   See warning for future changes.
#'   If “xy” is selected, the first dimension corresponds to the cardinality of 
#'   the second input and the second dimension corresponds to the cardinality of the 
#'   first input.
#'   If “ij” is selected, the dimensions are in the same order as the cardinality
#'   of the inputs.
#'   
#' @section Warning:
#' In the future `torch_meshgrid` will transition to indexing=’xy’ as the default.
#' This [issue](https://github.com/pytorch/pytorch/issues/50276) tracks this issue
#' with the goal of migrating to NumPy’s behavior.
#'
#' @name torch_meshgrid
#'
#' @export
NULL


#' Cartesian_prod
#'
#' Do cartesian product of the given sequence of tensors.
#'
#' @param tensors a list containing any number of 1 dimensional tensors.
#'
#' @name torch_cartesian_prod
#'
#' @export
NULL


#' Combinations
#'
#' @section combinations(input, r=2, with_replacement=False) -> seq :
#'
#' Compute combinations of length \eqn{r} of the given tensor. The behavior is similar to
#' python's `itertools.combinations` when `with_replacement` is set to `False`, and
#' `itertools.combinations_with_replacement` when `with_replacement` is set to `TRUE`.
#'
#'
#' @param self (Tensor) 1D vector.
#' @param r (int, optional) number of elements to combine
#' @param with_replacement (boolean, optional) whether to allow duplication in combination
#'
#' @name torch_combinations
#'
#' @export
NULL


#' Result_type
#'
#' @section result_type(tensor1, tensor2) -> dtype :
#'
#' Returns the `torch_dtype` that would result from performing an arithmetic
#' operation on the provided input tensors. See type promotion documentation 
#' for more information on the type promotion logic.
#'
#' @param tensor1 (Tensor or Number) an input tensor or number
#' @param tensor2 (Tensor or Number) an input tensor or number
#' 
#'
#' @name torch_result_type
#'
#' @export
NULL


#' Can_cast
#'
#' @section can_cast(from, to) -> bool :
#'
#' Determines if a type conversion is allowed under PyTorch casting rules
#' described in the type promotion documentation .
#'
#'
#' @param from (dtype) The original `torch_dtype`.
#' @param to (dtype) The target `torch_dtype`.
#'
#' @name torch_can_cast
#'
#' @export
NULL


#' Promote_types
#'
#' @section promote_types(type1, type2) -> dtype :
#'
#' Returns the `torch_dtype` with the smallest size and scalar kind that is
#' not smaller nor of lower kind than either `type1` or `type2`. See type promotion
#' documentation  for more information on the type
#' promotion logic.
#'
#'
#' @param type1 (`torch.dtype`) 
#' @param type2 (`torch.dtype`) 
#'
#' @name torch_promote_types
#'
#' @export
NULL


#' Bitwise_and
#'
#' @section bitwise_and(input, other, out=NULL) -> Tensor :
#'
#' Computes the bitwise AND of `input` and `other`. The input tensor must be of
#' integral or Boolean types. For bool tensors, it computes the logical AND.
#'
#'
#' @param self NA the first input tensor
#' @param other NA the second input tensor
#' 
#'
#' @name torch_bitwise_and
#'
#' @export
NULL


#' Bitwise_or
#'
#' @section bitwise_or(input, other, out=NULL) -> Tensor :
#'
#' Computes the bitwise OR of `input` and `other`. The input tensor must be of
#' integral or Boolean types. For bool tensors, it computes the logical OR.
#'
#'
#' @param self NA the first input tensor
#' @param other NA the second input tensor
#' 
#'
#' @name torch_bitwise_or
#'
#' @export
NULL


#' Bitwise_xor
#'
#' @section bitwise_xor(input, other, out=NULL) -> Tensor :
#'
#' Computes the bitwise XOR of `input` and `other`. The input tensor must be of
#' integral or Boolean types. For bool tensors, it computes the logical XOR.
#'
#'
#' @param self NA the first input tensor
#' @param other NA the second input tensor
#' 
#'
#' @name torch_bitwise_xor
#'
#' @export
NULL


#' Addbmm
#'
#' @section addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor :
#'
#' Performs a batch matrix-matrix product of matrices stored
#' in `batch1` and `batch2`,
#' with a reduced add step (all matrix multiplications get accumulated
#' along the first dimension).
#' `input` is added to the final result.
#' 
#' `batch1` and `batch2` must be 3-D tensors each containing the
#' same number of matrices.
#' 
#' If `batch1` is a \eqn{(b \times n \times m)} tensor, `batch2` is a
#' \eqn{(b \times m \times p)} tensor, `input` must be
#' broadcastable  with a \eqn{(n \times p)} tensor
#' and `out` will be a \eqn{(n \times p)} tensor.
#' 
#' \deqn{
#'     out = \beta\ \mbox{input} + \alpha\ (\sum_{i=0}^{b-1} \mbox{batch1}_i \mathbin{@} \mbox{batch2}_i)
#' }
#' For inputs of type `FloatTensor` or `DoubleTensor`, arguments `beta` and `alpha`
#' must be real numbers, otherwise they should be integers.
#'
#'
#' @param batch1 (Tensor) the first batch of matrices to be multiplied
#' @param batch2 (Tensor) the second batch of matrices to be multiplied
#' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
#' @param self (Tensor) matrix to be added
#' @param alpha (Number, optional) multiplier for `batch1 @ batch2` (\eqn{\alpha})
#' 
#'
#' @name torch_addbmm
#'
#' @export
NULL


#' Diag
#'
#' @section diag(input, diagonal=0, out=NULL) -> Tensor :
#'
#' - If `input` is a vector (1-D tensor), then returns a 2-D square tensor
#'   with the elements of `input` as the diagonal.
#' - If `input` is a matrix (2-D tensor), then returns a 1-D tensor with
#'   the diagonal elements of `input`.
#' 
#' The argument `diagonal` controls which diagonal to consider:
#' 
#' - If `diagonal` = 0, it is the main diagonal.
#' - If `diagonal` > 0, it is above the main diagonal.
#' - If `diagonal` < 0, it is below the main diagonal.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param diagonal (int, optional) the diagonal to consider
#' 
#'
#' @name torch_diag
#'
#' @export
NULL


#' Cross
#'
#' @section cross(input, other, dim=-1, out=NULL) -> Tensor :
#'
#' Returns the cross product of vectors in dimension `dim` of `input`
#' and `other`.
#' 
#' `input` and `other` must have the same size, and the size of their
#' `dim` dimension should be 3.
#' 
#' If `dim` is not given, it defaults to the first dimension found with the
#' size 3.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param other (Tensor) the second input tensor
#' @param dim (int, optional) the dimension to take the cross-product in.
#' 
#'
#' @name torch_cross
#'
#' @export
NULL


#' Triu
#'
#' @section triu(input, diagonal=0, out=NULL) -> Tensor :
#'
#' Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
#' `input`, the other elements of the result tensor `out` are set to 0.
#' 
#' The upper triangular part of the matrix is defined as the elements on and
#' above the diagonal.
#' 
#' The argument `diagonal` controls which diagonal to consider. If
#' `diagonal` = 0, all elements on and above the main diagonal are
#' retained. A positive value excludes just as many diagonals above the main
#' diagonal, and similarly a negative value includes just as many diagonals below
#' the main diagonal. The main diagonal are the set of indices
#' \eqn{\lbrace (i, i) \rbrace} for \eqn{i \in [0, \min\{d_{1}, d_{2}\} - 1]} where
#' \eqn{d_{1}, d_{2}} are the dimensions of the matrix.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param diagonal (int, optional) the diagonal to consider
#' 
#'
#' @name torch_triu
#'
#' @export
NULL


#' Tril
#'
#' @section tril(input, diagonal=0, out=NULL) -> Tensor :
#'
#' Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices
#' `input`, the other elements of the result tensor `out` are set to 0.
#' 
#' The lower triangular part of the matrix is defined as the elements on and
#' below the diagonal.
#' 
#' The argument `diagonal` controls which diagonal to consider. If
#' `diagonal` = 0, all elements on and below the main diagonal are
#' retained. A positive value includes just as many diagonals above the main
#' diagonal, and similarly a negative value excludes just as many diagonals below
#' the main diagonal. The main diagonal are the set of indices
#' \eqn{\lbrace (i, i) \rbrace} for \eqn{i \in [0, \min\{d_{1}, d_{2}\} - 1]} where
#' \eqn{d_{1}, d_{2}} are the dimensions of the matrix.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param diagonal (int, optional) the diagonal to consider
#' 
#'
#' @name torch_tril
#'
#' @export
NULL


#' Tril_indices
#'
#' @section tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor :
#'
#' Returns the indices of the lower triangular part of a `row`-by-
#' `col` matrix in a 2-by-N Tensor, where the first row contains row
#' coordinates of all indices and the second row contains column coordinates.
#' Indices are ordered based on rows and then columns.
#' 
#' The lower triangular part of the matrix is defined as the elements on and
#' below the diagonal.
#' 
#' The argument `offset` controls which diagonal to consider. If
#' `offset` = 0, all elements on and below the main diagonal are
#' retained. A positive value includes just as many diagonals above the main
#' diagonal, and similarly a negative value excludes just as many diagonals below
#' the main diagonal. The main diagonal are the set of indices
#' \eqn{\lbrace (i, i) \rbrace} for \eqn{i \in [0, \min\{d_{1}, d_{2}\} - 1]}
#' where \eqn{d_{1}, d_{2}} are the dimensions of the matrix.
#' 
#' @note
#'     When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
#'     prevent overflow during calculation.
#'
#'
#' @param row (`int`) number of rows in the 2-D matrix.
#' @param col (`int`) number of columns in the 2-D matrix.
#' @param offset (`int`) diagonal offset from the main diagonal.        Default: if not provided, 0.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, `torch_long`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param layout (`torch.layout`, optional) currently only support `torch_strided`.
#'
#' @name torch_tril_indices
#'
#' @export
NULL


#' Triu_indices
#'
#' @section triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor :
#'
#' Returns the indices of the upper triangular part of a `row` by
#' `col` matrix in a 2-by-N Tensor, where the first row contains row
#' coordinates of all indices and the second row contains column coordinates.
#' Indices are ordered based on rows and then columns.
#' 
#' The upper triangular part of the matrix is defined as the elements on and
#' above the diagonal.
#' 
#' The argument `offset` controls which diagonal to consider. If
#' `offset` = 0, all elements on and above the main diagonal are
#' retained. A positive value excludes just as many diagonals above the main
#' diagonal, and similarly a negative value includes just as many diagonals below
#' the main diagonal. The main diagonal are the set of indices
#' \eqn{\lbrace (i, i) \rbrace} for \eqn{i \in [0, \min\{d_{1}, d_{2}\} - 1]}
#' where \eqn{d_{1}, d_{2}} are the dimensions of the matrix.
#' 
#' @note
#'     When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
#'     prevent overflow during calculation.
#'
#'
#' @param row (`int`) number of rows in the 2-D matrix.
#' @param col (`int`) number of columns in the 2-D matrix.
#' @param offset (`int`) diagonal offset from the main diagonal.        Default: if not provided, 0.
#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, `torch_long`.
#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
#' @param layout (`torch.layout`, optional) currently only support `torch_strided`.
#'
#' @name torch_triu_indices
#'
#' @export
NULL


#' Trace
#'
#' @section trace(input) -> Tensor :
#'
#' Returns the sum of the elements of the diagonal of the input 2-D matrix.
#'
#' @param self the input tensor
#'
#' @name torch_trace
#'
#' @export
NULL


#' Ne
#'
#' @section ne(input, other, out=NULL) -> Tensor :
#'
#' Computes \eqn{input \neq other} element-wise.
#' 
#' The second argument can be a number or a tensor whose shape is
#' broadcastable  with the first argument.
#'
#'
#' @param self (Tensor) the tensor to compare
#' @param other (Tensor or float) the tensor or value to compare
#'
#' @name torch_ne
#'
#' @export
NULL


#' Eq
#'
#' @section eq(input, other, out=NULL) -> Tensor :
#'
#' Computes element-wise equality
#' 
#' The second argument can be a number or a tensor whose shape is
#' broadcastable  with the first argument.
#'
#'
#' @param self (Tensor) the tensor to compare
#' @param other (Tensor or float) the tensor or value to compare
#'  Must be a `ByteTensor`
#'
#' @name torch_eq
#'
#' @export
NULL


#' Ge
#'
#' @section ge(input, other, out=NULL) -> Tensor :
#'
#' Computes \eqn{\mbox{input} \geq \mbox{other}} element-wise.
#' 
#' The second argument can be a number or a tensor whose shape is
#' broadcastable  with the first argument.
#'
#'
#' @param self (Tensor) the tensor to compare
#' @param other (Tensor or float) the tensor or value to compare
#'
#' @name torch_ge
#'
#' @export
NULL


#' Le
#'
#' @section le(input, other, out=NULL) -> Tensor :
#'
#' Computes \eqn{\mbox{input} \leq \mbox{other}} element-wise.
#' 
#' The second argument can be a number or a tensor whose shape is
#' broadcastable  with the first argument.
#'
#'
#' @param self (Tensor) the tensor to compare
#' @param other (Tensor or float) the tensor or value to compare
#'
#' @name torch_le
#'
#' @export
NULL


#' Gt
#'
#' @section gt(input, other, out=NULL) -> Tensor :
#'
#' Computes \eqn{\mbox{input} > \mbox{other}} element-wise.
#' 
#' The second argument can be a number or a tensor whose shape is
#' broadcastable  with the first argument.
#'
#'
#' @param self (Tensor) the tensor to compare
#' @param other (Tensor or float) the tensor or value to compare
#'
#' @name torch_gt
#'
#' @export
NULL


#' Lt
#'
#' @section lt(input, other, out=NULL) -> Tensor :
#'
#' Computes \eqn{\mbox{input} < \mbox{other}} element-wise.
#' 
#' The second argument can be a number or a tensor whose shape is
#' broadcastable  with the first argument.
#'
#'
#' @param self (Tensor) the tensor to compare
#' @param other (Tensor or float) the tensor or value to compare
#'
#' @name torch_lt
#'
#' @export
NULL


#' Take
#'
#' @section take(input, index) -> Tensor :
#'
#' Returns a new tensor with the elements of `input` at the given indices.
#' The input tensor is treated as if it were viewed as a 1-D tensor. The result
#' takes the same shape as the indices.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param index (LongTensor) the indices into tensor
#'
#' @name torch_take
#'
#' @export
NULL


#' Index_select
#'
#' @section index_select(input, dim, index, out=NULL) -> Tensor :
#'
#' Returns a new tensor which indexes the `input` tensor along dimension
#' `dim` using the entries in `index` which is a `LongTensor`.
#' 
#' The returned tensor has the same number of dimensions as the original tensor
#' (`input`).  The `dim`\ th dimension has the same size as the length
#' of `index`; other dimensions have the same size as in the original tensor.
#' 
#' @note The returned tensor does **not** use the same storage as the original
#'           tensor.  If `out` has a different shape than expected, we
#'           silently change it to the correct shape, reallocating the underlying
#'           storage if necessary.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int) the dimension in which we index
#' @param index (LongTensor) the 1-D tensor containing the indices to index
#' 
#'
#' @name torch_index_select
#'
#' @export
NULL


#' Masked_select
#'
#' @section masked_select(input, mask, out=NULL) -> Tensor :
#'
#' Returns a new 1-D tensor which indexes the `input` tensor according to
#' the boolean mask `mask` which is a `BoolTensor`.
#' 
#' The shapes of the `mask` tensor and the `input` tensor don't need
#' to match, but they must be broadcastable .
#' 
#' @note The returned tensor does **not** use the same storage
#'           as the original tensor
#'
#'
#' @param self (Tensor) the input tensor.
#' @param mask (BoolTensor) the tensor containing the binary mask to index with
#' 
#'
#' @name torch_masked_select
#'
#' @export
NULL


#' Nonzero
#' 
#' Nonzero elements of tensors.
#' 
#' @param self (Tensor) the input tensor.
#' @param as_list If `FALSE`, the output tensor containing indices. If `TRUE`, one 
#'   1-D tensor for each dimension, containing the indices of each nonzero element 
#'   along that dimension.
#'
#' **When** `as_list` **is `FALSE` (default)**:
#' 
#' Returns a tensor containing the indices of all non-zero elements of
#' `input`.  Each row in the result contains the indices of a non-zero
#' element in `input`. The result is sorted lexicographically, with
#' the last index changing the fastest (C-style).
#' 
#' If `input` has \eqn{n} dimensions, then the resulting indices tensor
#' `out` is of size \eqn{(z \times n)}, where \eqn{z} is the total number of
#' non-zero elements in the `input` tensor.
#' 
#' **When** `as_list` **is `TRUE`**:
#' 
#' Returns a tuple of 1-D tensors, one for each dimension in `input`,
#' each containing the indices (in that dimension) of all non-zero elements of
#' `input` .
#' 
#' If `input` has \eqn{n} dimensions, then the resulting tuple contains \eqn{n}
#' tensors of size \eqn{z}, where \eqn{z} is the total number of
#' non-zero elements in the `input` tensor.
#' 
#' As a special case, when `input` has zero dimensions and a nonzero scalar
#' value, it is treated as a one-dimensional tensor with one element.
#'
#'
#' @name torch_nonzero
#'
#' @export
NULL


#' Gather
#'
#' @section gather(input, dim, index, sparse_grad=FALSE) -> Tensor :
#'
#' Gathers values along an axis specified by `dim`.
#' 
#' For a 3-D tensor the output is specified by::
#' 
#'     out[i][j][k] = input[index[i][j][k]][j][k]  # if dim == 0
#'     out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1
#'     out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2
#' 
#' If `input` is an n-dimensional tensor with size
#' \eqn{(x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})}
#' and `dim = i`, then `index` must be an \eqn{n}-dimensional tensor with
#' size \eqn{(x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})} where \eqn{y \geq 1}
#' and `out` will have the same size as `index`.
#'
#'
#' @param self (Tensor) the source tensor
#' @param dim (int) the axis along which to index
#' @param index (LongTensor) the indices of elements to gather
#' @param sparse_grad (bool,optional) If `TRUE`, gradient w.r.t. `input` will be a sparse tensor.
#'
#' @name torch_gather
#'
#' @export
NULL


#' Addcmul
#'
#' @section addcmul(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor :
#'
#' Performs the element-wise multiplication of `tensor1`
#' by `tensor2`, multiply the result by the scalar `value`
#' and add it to `input`.
#' 
#' \deqn{
#'     \mbox{out}_i = \mbox{input}_i + \mbox{value} \times \mbox{tensor1}_i \times \mbox{tensor2}_i
#' }
#' The shapes of `tensor`, `tensor1`, and `tensor2` must be
#' broadcastable .
#' 
#' For inputs of type `FloatTensor` or `DoubleTensor`, `value` must be
#' a real number, otherwise an integer.
#'
#'
#' @param self (Tensor) the tensor to be added
#' @param tensor1 (Tensor) the tensor to be multiplied
#' @param tensor2 (Tensor) the tensor to be multiplied
#' @param value (Number, optional) multiplier for \eqn{tensor1 .* tensor2}
#' 
#'
#' @name torch_addcmul
#'
#' @export
NULL


#' Addcdiv
#'
#' @section addcdiv(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor :
#'
#' Performs the element-wise division of `tensor1` by `tensor2`,
#' multiply the result by the scalar `value` and add it to `input`.
#' 
#' @section Warning:
#'     Integer division with addcdiv is deprecated, and in a future release
#'     addcdiv will perform a true division of `tensor1` and `tensor2`.
#'     The current addcdiv behavior can be replicated using [torch_floor_divide()]
#'     for integral inputs
#'     (`input` + `value` * `tensor1` // `tensor2`)
#'     and [torch_div()] for float inputs
#'     (`input` + `value` * `tensor1` / `tensor2`).
#'     The new addcdiv behavior can be implemented with [torch_true_divide()]
#'     (`input` + `value` * torch.true_divide(`tensor1`,
#'     `tensor2`).
#' 
#' \deqn{
#'     \mbox{out}_i = \mbox{input}_i + \mbox{value} \times \frac{\mbox{tensor1}_i}{\mbox{tensor2}_i}
#' }
#' 
#' The shapes of `input`, `tensor1`, and `tensor2` must be
#' broadcastable .
#' 
#' For inputs of type `FloatTensor` or `DoubleTensor`, `value` must be
#' a real number, otherwise an integer.
#'
#'
#' @param self (Tensor) the tensor to be added
#' @param tensor1 (Tensor) the numerator tensor
#' @param tensor2 (Tensor) the denominator tensor
#' @param value (Number, optional) multiplier for \eqn{\mbox{tensor1} / \mbox{tensor2}}
#' 
#'
#' @name torch_addcdiv
#'
#' @export
NULL


#' Lstsq
#'
#' @section lstsq(input, A, out=NULL) -> Tensor :
#'
#' Computes the solution to the least squares and least norm problems for a full
#' rank matrix \eqn{A} of size \eqn{(m \times n)} and a matrix \eqn{B} of
#' size \eqn{(m \times k)}.
#' 
#' If \eqn{m \geq n}, [torch_lstsq()] solves the least-squares problem:
#' 
#' \deqn{
#'    \begin{array}{ll}
#'    \min_X & \|AX-B\|_2.
#'    \end{array}
#' }
#' If \eqn{m < n}, [torch_lstsq()] solves the least-norm problem:
#' 
#' \deqn{
#'    \begin{array}{llll}
#'    \min_X & \|X\|_2 & \mbox{subject to} & AX = B.
#'    \end{array}
#' }
#' Returned tensor \eqn{X} has shape \eqn{(\mbox{max}(m, n) \times k)}. The first \eqn{n}
#' rows of \eqn{X} contains the solution. If \eqn{m \geq n}, the residual sum of squares
#' for the solution in each column is given by the sum of squares of elements in the
#' remaining \eqn{m - n} rows of that column.
#' 
#' @note
#'     The case when \eqn{m < n} is not supported on the GPU.
#'
#'
#' @param self (Tensor) the matrix \eqn{B}
#' @param A (Tensor) the \eqn{m} by \eqn{n} matrix \eqn{A}
#'
#' @name torch_lstsq
NULL


#' Triangular_solve
#'
#' @section triangular_solve(input, A, upper=TRUE, transpose=False, unitriangular=False) -> (Tensor, Tensor) :
#'
#' Solves a system of equations with a triangular coefficient matrix \eqn{A}
#' and multiple right-hand sides \eqn{b}.
#' 
#' In particular, solves \eqn{AX = b} and assumes \eqn{A} is upper-triangular
#' with the default keyword arguments.
#' 
#' `torch_triangular_solve(b, A)` can take in 2D inputs `b, A` or inputs that are
#' batches of 2D matrices. If the inputs are batches, then returns
#' batched outputs `X`
#'
#'
#' @param self (Tensor) multiple right-hand sides of size \eqn{(*, m, k)} where                \eqn{*} is zero of more batch dimensions (\eqn{b})
#' @param A (Tensor) the input triangular coefficient matrix of size \eqn{(*, m, m)}                where \eqn{*} is zero or more batch dimensions
#' @param upper (bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: `TRUE`.
#' @param transpose (bool, optional) whether \eqn{A} should be transposed before        being sent into the solver. Default: `FALSE`.
#' @param unitriangular (bool, optional) whether \eqn{A} is unit triangular.        If TRUE, the diagonal elements of \eqn{A} are assumed to be        1 and not referenced from \eqn{A}. Default: `FALSE`.
#'
#' @name torch_triangular_solve
#'
#' @export
NULL


#' Symeig
#'
#' @section symeig(input, eigenvectors=False, upper=TRUE) -> (Tensor, Tensor) :
#'
#' This function returns eigenvalues and eigenvectors
#' of a real symmetric matrix `input` or a batch of real symmetric matrices,
#' represented by a namedtuple (eigenvalues, eigenvectors).
#' 
#' This function calculates all eigenvalues (and vectors) of `input`
#' such that \eqn{\mbox{input} = V \mbox{diag}(e) V^T}.
#' 
#' The boolean argument `eigenvectors` defines computation of
#' both eigenvectors and eigenvalues or eigenvalues only.
#' 
#' If it is `FALSE`, only eigenvalues are computed. If it is `TRUE`,
#' both eigenvalues and eigenvectors are computed.
#' 
#' Since the input matrix `input` is supposed to be symmetric,
#' only the upper triangular portion is used by default.
#' 
#' If `upper` is `FALSE`, then lower triangular portion is used.
#' 
#' @note The eigenvalues are returned in ascending order. If `input` is a batch of matrices,
#'           then the eigenvalues of each matrix in the batch is returned in ascending order.
#' 
#' @note Irrespective of the original strides, the returned matrix `V` will
#'           be transposed, i.e. with strides `V.contiguous().transpose(-1, -2).stride()`.
#' 
#' @note Extra care needs to be taken when backward through outputs. Such
#'           operation is really only stable when all eigenvalues are distinct.
#'           Otherwise, `NaN` can appear as the gradients are not properly defined.
#'
#'
#' @param self (Tensor) the input tensor of size \eqn{(*, n, n)} where `*` is zero or more                    batch dimensions consisting of symmetric matrices.
#' @param eigenvectors (boolean, optional) controls whether eigenvectors have to be computed
#' @param upper (boolean, optional) controls whether to consider upper-triangular or lower-triangular region
#'
#' @name torch_symeig
#'
#' @export
NULL


#' Eig
#'
#' @section eig(input, eigenvectors=False, out=NULL) -> (Tensor, Tensor) :
#'
#' Computes the eigenvalues and eigenvectors of a real square matrix.
#' 
#' @note
#'     Since eigenvalues and eigenvectors might be complex, backward pass is supported only
#'     for [`torch_symeig`]
#'
#'
#' @param self (Tensor) the square matrix of shape \eqn{(n \times n)} for which the eigenvalues and eigenvectors        will be computed
#' @param eigenvectors (bool) `TRUE` to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed
#'
#' @name torch_eig
#'
NULL


#' Svd
#'
#' @section svd(input, some=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor) :
#'
#' This function returns a namedtuple `(U, S, V)` which is the singular value
#' decomposition of a input real matrix or batches of real matrices `input` such that
#' \eqn{input = U \times diag(S) \times V^T}.
#' 
#' If `some` is `TRUE` (default), the method returns the reduced singular value decomposition
#' i.e., if the last two dimensions of `input` are `m` and `n`, then the returned
#' `U` and `V` matrices will contain only \eqn{min(n, m)} orthonormal columns.
#' 
#' If `compute_uv` is `FALSE`, the returned `U` and `V` matrices will be zero matrices
#' of shape \eqn{(m \times m)} and \eqn{(n \times n)} respectively. `some` will be ignored here.
#' 
#' @note The singular values are returned in descending order. If `input` is a batch of matrices,
#'           then the singular values of each matrix in the batch is returned in descending order.
#' 
#' @note The implementation of SVD on CPU uses the LAPACK routine `?gesdd` (a divide-and-conquer
#'           algorithm) instead of `?gesvd` for speed. Analogously, the SVD on GPU uses the MAGMA routine
#'           `gesdd` as well.
#' 
#' @note Irrespective of the original strides, the returned matrix `U`
#'           will be transposed, i.e. with strides `U.contiguous().transpose(-2, -1).stride()`
#' 
#' @note Extra care needs to be taken when backward through `U` and `V`
#'           outputs. Such operation is really only stable when `input` is
#'           full rank with all distinct singular values. Otherwise, `NaN` can
#'           appear as the gradients are not properly defined. Also, notice that
#'           double backward will usually do an additional backward through `U` and
#'           `V` even if the original backward is only on `S`.
#' 
#' @note When `some` = `FALSE`, the gradients on `U[..., :, min(m, n):]`
#'           and `V[..., :, min(m, n):]` will be ignored in backward as those vectors
#'           can be arbitrary bases of the subspaces.
#' 
#' @note When `compute_uv` = `FALSE`, backward cannot be performed since `U` and `V`
#'           from the forward pass is required for the backward operation.
#'
#'
#' @param self (Tensor) the input tensor of size \eqn{(*, m, n)} where `*` is zero or more                    batch dimensions consisting of \eqn{m \times n} matrices.
#' @param some (bool, optional) controls the shape of returned `U` and `V`
#' @param compute_uv (bool, optional) option whether to compute `U` and `V` or not
#'
#' @name torch_svd
#'
#' @export
NULL


#' Cholesky
#'
#' @section cholesky(input, upper=False, out=NULL) -> Tensor :
#'
#' Computes the Cholesky decomposition of a symmetric positive-definite
#' matrix \eqn{A} or for batches of symmetric positive-definite matrices.
#' 
#' If `upper` is `TRUE`, the returned matrix `U` is upper-triangular, and
#' the decomposition has the form:
#' 
#' \deqn{
#'   A = U^TU
#' }
#' If `upper` is `FALSE`, the returned matrix `L` is lower-triangular, and
#' the decomposition has the form:
#' 
#' \deqn{
#'     A = LL^T
#' }
#' If `upper` is `TRUE`, and \eqn{A} is a batch of symmetric positive-definite
#' matrices, then the returned tensor will be composed of upper-triangular Cholesky factors
#' of each of the individual matrices. Similarly, when `upper` is `FALSE`, the returned
#' tensor will be composed of lower-triangular Cholesky factors of each of the individual
#' matrices.
#'
#'
#' @param self (Tensor) the input tensor \eqn{A} of size \eqn{(*, n, n)} where `*` is zero or more                
#'   batch dimensions consisting of symmetric positive-definite matrices.
#' @param upper (bool, optional) flag that indicates whether to return a                            
#'   upper or lower triangular matrix. Default: `FALSE`
#'
#' @name torch_cholesky
#'
#' @export
NULL


#' Cholesky_solve
#'
#' @section cholesky_solve(input, input2, upper=False, out=NULL) -> Tensor :
#'
#' Solves a linear system of equations with a positive semidefinite
#' matrix to be inverted given its Cholesky factor matrix \eqn{u}.
#' 
#' If `upper` is `FALSE`, \eqn{u} is and lower triangular and `c` is
#' returned such that:
#' 
#' \deqn{
#'     c = (u u^T)^{{-1}} b
#' }
#' If `upper` is `TRUE` or not provided, \eqn{u} is upper triangular
#' and `c` is returned such that:
#' 
#' \deqn{
#'     c = (u^T u)^{{-1}} b
#' }
#' `torch_cholesky_solve(b, u)` can take in 2D inputs `b, u` or inputs that are
#' batches of 2D matrices. If the inputs are batches, then returns
#' batched outputs `c`
#'
#'
#' @param self (Tensor) input matrix \eqn{b} of size \eqn{(*, m, k)},                where \eqn{*} is zero or more batch dimensions
#' @param input2 (Tensor) input matrix \eqn{u} of size \eqn{(*, m, m)},                where \eqn{*} is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor
#' @param upper (bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: `FALSE`.
#'
#' @name torch_cholesky_solve
#'
#' @export
NULL

#' Cholesky_inverse
#'
#' @section cholesky_inverse(input, upper=False, out=NULL) -> Tensor :
#'
#' Computes the inverse of a symmetric positive-definite matrix \eqn{A} using its
#' Cholesky factor \eqn{u}: returns matrix `inv`. The inverse is computed using
#' LAPACK routines `dpotri` and `spotri` (and the corresponding MAGMA routines).
#' 
#' If `upper` is `FALSE`, \eqn{u} is lower triangular
#' such that the returned tensor is
#' 
#' \deqn{
#'     inv = (uu^{{T}})^{{-1}}
#' }
#' If `upper` is `TRUE` or not provided, \eqn{u} is upper
#' triangular such that the returned tensor is
#' 
#' \deqn{
#'     inv = (u^T u)^{{-1}}
#' }
#'
#'
#' @param self (Tensor) the input 2-D tensor \eqn{u}, a upper or lower triangular           Cholesky factor
#' @param upper (bool, optional) whether to return a lower (default) or upper triangular matrix
#'
#' @name torch_cholesky_inverse
#'
#' @export
NULL


#' Qr
#'
#' @section qr(input, some=TRUE, out=NULL) -> (Tensor, Tensor) :
#'
#' Computes the QR decomposition of a matrix or a batch of matrices `input`,
#' and returns a namedtuple (Q, R) of tensors such that \eqn{\mbox{input} = Q R}
#' with \eqn{Q} being an orthogonal matrix or batch of orthogonal matrices and
#' \eqn{R} being an upper triangular matrix or batch of upper triangular matrices.
#' 
#' If `some` is `TRUE`, then this function returns the thin (reduced) QR factorization.
#' Otherwise, if `some` is `FALSE`, this function returns the complete QR factorization.
#' 
#' @note precision may be lost if the magnitudes of the elements of `input`
#'           are large
#' 
#' @note While it should always give you a valid decomposition, it may not
#'           give you the same one across platforms - it will depend on your
#'           LAPACK implementation.
#'
#'
#' @param self (Tensor) the input tensor of size \eqn{(*, m, n)} where `*` is zero or more                batch dimensions consisting of matrices of dimension \eqn{m \times n}.
#' @param some (bool, optional) Set to `TRUE` for reduced QR decomposition and `FALSE` for                complete QR decomposition.
#'
#' @name torch_qr
#'
#' @export
NULL


#' Geqrf
#'
#' @section geqrf(input, out=NULL) -> (Tensor, Tensor) :
#'
#' This is a low-level function for calling LAPACK directly. This function
#' returns a namedtuple (a, tau) as defined in `LAPACK documentation for geqrf`_ .
#' 
#' You'll generally want to use [`torch_qr`] instead.
#' 
#' Computes a QR decomposition of `input`, but without constructing
#' \eqn{Q} and \eqn{R} as explicit separate matrices.
#' 
#' Rather, this directly calls the underlying LAPACK function `?geqrf`
#' which produces a sequence of 'elementary reflectors'.
#' 
#' See `LAPACK documentation for geqrf`_ for further details.
#'
#'
#' @param self (Tensor) the input matrix
#'
#' @name torch_geqrf
#'
#' @export
NULL


#' Orgqr
#'
#' @section orgqr(input, input2) -> Tensor :
#'
#' Computes the orthogonal matrix `Q` of a QR factorization, from the `(input, input2)`
#' tuple returned by [`torch_geqrf`].
#' 
#' This directly calls the underlying LAPACK function `?orgqr`.
#' See `LAPACK documentation for orgqr`_ for further details.
#'
#'
#' @param self (Tensor) the `a` from [`torch_geqrf`].
#' @param input2 (Tensor) the `tau` from [`torch_geqrf`].
#'
#' @name torch_orgqr
#'
#' @export
NULL


#' Ormqr
#'
#' @section ormqr(input, input2, input3, left=TRUE, transpose=False) -> Tensor :
#'
#' Multiplies `mat` (given by `input3`) by the orthogonal `Q` matrix of the QR factorization
#' formed by [torch_geqrf()] that is represented by `(a, tau)` (given by (`input`, `input2`)).
#' 
#' This directly calls the underlying LAPACK function `?ormqr`.
#' See [LAPACK documentation for ormqr](https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-0/overview.html) for further details.
#'
#'
#' @param self (Tensor) the `a` from [`torch_geqrf`].
#' @param input2 (Tensor) the `tau` from [`torch_geqrf`].
#' @param input3 (Tensor) the matrix to be multiplied.
#' @param left see LAPACK documentation
#' @param transpose see LAPACK documentation
#'
#' @name torch_ormqr
#'
#' @export
NULL


#' Lu_solve
#'
#' @section lu_solve(input, LU_data, LU_pivots, out=NULL) -> Tensor :
#'
#' Returns the LU solve of the linear system \eqn{Ax = b} using the partially pivoted
#' LU factorization of A from `torch_lu`.
#'
#'
#' @param self (Tensor) the RHS tensor of size \eqn{(*, m, k)}, where \eqn{*}                is zero or more batch dimensions.
#' @param LU_data (Tensor) the pivoted LU factorization of A from `torch_lu` of size \eqn{(*, m, m)},                       where \eqn{*} is zero or more batch dimensions.
#' @param LU_pivots (IntTensor) the pivots of the LU factorization from `torch_lu` of size \eqn{(*, m)},                           where \eqn{*} is zero or more batch dimensions.                           The batch dimensions of `LU_pivots` must be equal to the batch dimensions of                           `LU_data`.
#' 
#'
#' @name torch_lu_solve
#'
#' @export
NULL


#' Multinomial
#'
#' @section multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -> LongTensor :
#'
#' Returns a tensor where each row contains `num_samples` indices sampled
#' from the multinomial probability distribution located in the corresponding row
#' of tensor `input`.
#' 
#' @note
#'     The rows of `input` do not need to sum to one (in which case we use
#'     the values as weights), but must be non-negative, finite and have
#'     a non-zero sum.
#' 
#' Indices are ordered from left to right according to when each was sampled
#' (first samples are placed in first column).
#' 
#' If `input` is a vector, `out` is a vector of size `num_samples`.
#' 
#' If `input` is a matrix with `m` rows, `out` is an matrix of shape
#' \eqn{(m \times \mbox{num\_samples})}.
#' 
#' If replacement is `TRUE`, samples are drawn with replacement.
#' 
#' If not, they are drawn without replacement, which means that when a
#' sample index is drawn for a row, it cannot be drawn again for that row.
#' 
#' @note
#'     When drawn without replacement, `num_samples` must be lower than
#'     number of non-zero elements in `input` (or the min number of non-zero
#'     elements in each row of `input` if it is a matrix).
#'
#'
#' @param self (Tensor) the input tensor containing probabilities
#' @param num_samples (int) number of samples to draw
#' @param replacement (bool, optional) whether to draw with replacement or not
#' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling
#' 
#'
#' @name torch_multinomial
#'
#' @export
NULL


#' Lgamma
#'
#' @section lgamma(input, out=NULL) -> Tensor :
#'
#' Computes the logarithm of the gamma function on `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \log \Gamma(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_lgamma
#'
#' @export
NULL


#' Digamma
#'
#' @section digamma(input, out=NULL) -> Tensor :
#'
#' Computes the logarithmic derivative of the gamma function on `input`.
#' 
#' \deqn{
#'     \psi(x) = \frac{d}{dx} \ln\left(\Gamma\left(x\right)\right) = \frac{\Gamma'(x)}{\Gamma(x)}
#' }
#'
#'
#' @param self (Tensor) the tensor to compute the digamma function on
#'
#' @name torch_digamma
#'
#' @export
NULL


#' Polygamma
#'
#' @section polygamma(n, input, out=NULL) -> Tensor :
#'
#' Computes the \eqn{n^{th}} derivative of the digamma function on `input`.
#' \eqn{n \geq 0} is called the order of the polygamma function.
#' 
#' \deqn{
#'     \psi^{(n)}(x) = \frac{d^{(n)}}{dx^{(n)}} \psi(x)
#' }
#' @note
#'     This function is not implemented for \eqn{n \geq 2}.
#'
#'
#' @param n (int) the order of the polygamma function
#' @param input (Tensor) the input tensor.
#' 
#'
#' @name torch_polygamma
#'
#' @export
NULL


#' Erfinv
#'
#' @section erfinv(input, out=NULL) -> Tensor :
#'
#' Computes the inverse error function of each element of `input`.
#' The inverse error function is defined in the range \eqn{(-1, 1)} as:
#' 
#' \deqn{
#'     \mathrm{erfinv}(\mathrm{erf}(x)) = x
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_erfinv
#'
#' @export
NULL


#' Sign
#'
#' @section sign(input, out=NULL) -> Tensor :
#'
#' Returns a new tensor with the signs of the elements of `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \mbox{sgn}(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' 
#'
#' @name torch_sign
#'
#' @export
NULL


#' Dist
#'
#' @section dist(input, other, p=2) -> Tensor :
#'
#' Returns the p-norm of (`input` - `other`)
#' 
#' The shapes of `input` and `other` must be
#' broadcastable .
#'
#'
#' @param self (Tensor) the input tensor.
#' @param other (Tensor) the Right-hand-side input tensor
#' @param p (float, optional) the norm to be computed
#'
#' @name torch_dist
#'
#' @export
NULL


#' Atan2
#'
#' @section atan2(input, other, out=NULL) -> Tensor :
#'
#' Element-wise arctangent of \eqn{\mbox{input}_{i} / \mbox{other}_{i}}
#' with consideration of the quadrant. Returns a new tensor with the signed angles
#' in radians between vector \eqn{(\mbox{other}_{i}, \mbox{input}_{i})}
#' and vector \eqn{(1, 0)}. (Note that \eqn{\mbox{other}_{i}}, the second
#' parameter, is the x-coordinate, while \eqn{\mbox{input}_{i}}, the first
#' parameter, is the y-coordinate.)
#' 
#' The shapes of `input` and `other` must be
#' broadcastable .
#'
#'
#' @param self (Tensor) the first input tensor
#' @param other (Tensor) the second input tensor
#' 
#'
#' @name torch_atan2
#'
#' @export
NULL


#' Lerp
#'
#' @section lerp(input, end, weight, out=NULL) :
#'
#' Does a linear interpolation of two tensors `start` (given by `input`) and `end` based
#' on a scalar or tensor `weight` and returns the resulting `out` tensor.
#' 
#' \deqn{
#'     \mbox{out}_i = \mbox{start}_i + \mbox{weight}_i \times (\mbox{end}_i - \mbox{start}_i)
#' }
#' The shapes of `start` and `end` must be
#' broadcastable . If `weight` is a tensor, then
#' the shapes of `weight`, `start`, and `end` must be broadcastable .
#'
#'
#' @param self (Tensor) the tensor with the starting points
#' @param end (Tensor) the tensor with the ending points
#' @param weight (float or tensor) the weight for the interpolation formula
#' 
#'
#' @name torch_lerp
#'
#' @export
NULL


#' Histc
#'
#' @section histc(input, bins=100, min=0, max=0, out=NULL) -> Tensor :
#'
#' Computes the histogram of a tensor.
#' 
#' The elements are sorted into equal width bins between `min` and
#' `max`. If `min` and `max` are both zero, the minimum and
#' maximum values of the data are used.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param bins (int) number of histogram bins
#' @param min (int) lower end of the range (inclusive)
#' @param max (int) upper end of the range (inclusive)
#' 
#'
#' @name torch_histc
#'
#' @export
NULL


#' Fmod
#'
#' @section fmod(input, other, out=NULL) -> Tensor :
#'
#' Computes the element-wise remainder of division.
#' 
#' The dividend and divisor may contain both for integer and floating point
#' numbers. The remainder has the same sign as the dividend `input`.
#' 
#' When `other` is a tensor, the shapes of `input` and
#' `other` must be broadcastable .
#'
#'
#' @param self (Tensor) the dividend
#' @param other (Tensor or float) the divisor, which may be either a number or a tensor of the same shape as the dividend
#' 
#'
#' @name torch_fmod
#'
#' @export
NULL


#' Remainder
#'
#' @section remainder(input, other, out=NULL) -> Tensor :
#'
#' Computes the element-wise remainder of division.
#' 
#' The divisor and dividend may contain both for integer and floating point
#' numbers. The remainder has the same sign as the divisor.
#' 
#' When `other` is a tensor, the shapes of `input` and
#' `other` must be broadcastable .
#'
#'
#' @param self (Tensor) the dividend
#' @param other (Tensor or float) the divisor that may be either a number or a                               Tensor of the same shape as the dividend
#' 
#'
#' @name torch_remainder
#'
#' @export
NULL


#' Sort
#'
#' @section sort(input, dim=-1, descending=FALSE) -> (Tensor, LongTensor) :
#'
#' Sorts the elements of the `input` tensor along a given dimension
#' in ascending order by value.
#' 
#' If `dim` is not given, the last dimension of the `input` is chosen.
#' 
#' If `descending` is `TRUE` then the elements are sorted in descending
#' order by value.
#' 
#' A namedtuple of (values, indices) is returned, where the `values` are the
#' sorted values and `indices` are the indices of the elements in the original
#' `input` tensor.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int, optional) the dimension to sort along
#' @param descending (bool, optional) controls the sorting order (ascending or descending)
#' @param stable (bool, optional) – makes the sorting routine stable, which guarantees 
#'   that the order of equivalent elements is preserved.
#'
#' @name torch_sort
#'
#' @export
NULL


#' Argsort
#'
#' @section argsort(input, dim=-1, descending=False) -> LongTensor :
#'
#' Returns the indices that sort a tensor along a given dimension in ascending
#' order by value.
#' 
#' This is the second value returned by `torch_sort`.  See its documentation
#' for the exact semantics of this method.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int, optional) the dimension to sort along
#' @param descending (bool, optional) controls the sorting order (ascending or descending)
#'
#' @name torch_argsort
#'
#' @export
NULL


#' Topk
#'
#' @section topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor) :
#'
#' Returns the `k` largest elements of the given `input` tensor along
#' a given dimension.
#' 
#' If `dim` is not given, the last dimension of the `input` is chosen.
#' 
#' If `largest` is `FALSE` then the `k` smallest elements are returned.
#' 
#' A namedtuple of `(values, indices)` is returned, where the `indices` are the indices
#' of the elements in the original `input` tensor.
#' 
#' The boolean option `sorted` if `TRUE`, will make sure that the returned
#' `k` elements are themselves sorted
#'
#'
#' @param self (Tensor) the input tensor.
#' @param k (int) the k in "top-k"
#' @param dim (int, optional) the dimension to sort along
#' @param largest (bool, optional) controls whether to return largest or           smallest elements
#' @param sorted (bool, optional) controls whether to return the elements           in sorted order
#'
#' @name torch_topk
#'
#' @export
NULL


#' Renorm
#'
#' @section renorm(input, p, dim, maxnorm, out=NULL) -> Tensor :
#'
#' Returns a tensor where each sub-tensor of `input` along dimension
#' `dim` is normalized such that the `p`-norm of the sub-tensor is lower
#' than the value `maxnorm`
#' 
#' @note If the norm of a row is lower than `maxnorm`, the row is unchanged
#'
#'
#' @param self (Tensor) the input tensor.
#' @param p (float) the power for the norm computation
#' @param dim (int) the dimension to slice over to get the sub-tensors
#' @param maxnorm (float) the maximum norm to keep each sub-tensor under
#' 
#'
#' @name torch_renorm
#'
#' @export
NULL


#' Equal
#'
#' @section equal(input, other) -> bool :
#'
#' `TRUE` if two tensors have the same size and elements, `FALSE` otherwise.
#'
#' @param self the input tensor
#' @param other the other input tensor
#'
#'
#'
#' @name torch_equal
#'
#' @export
NULL


#' Normal
#'
#' @section normal(mean, std, *) -> Tensor :
#'
#' Returns a tensor of random numbers drawn from separate normal distributions
#' whose mean and standard deviation are given.
#' 
#' The `mean` is a tensor with the mean of
#' each output element's normal distribution
#' 
#' The `std` is a tensor with the standard deviation of
#' each output element's normal distribution
#' 
#' The shapes of `mean` and `std` don't need to match, but the
#' total number of elements in each tensor need to be the same.
#' 
#' @note When the shapes do not match, the shape of `mean`
#'       is used as the shape for the returned output tensor
#'
#' @section normal(mean=0.0, std) -> Tensor :
#'
#' Similar to the function above, but the means are shared among all drawn
#' elements.
#'
#' @section normal(mean, std=1.0) -> Tensor :
#'
#' Similar to the function above, but the standard-deviations are shared among
#' all drawn elements.
#'
#' @section normal(mean, std, size, *) -> Tensor :
#'
#' Similar to the function above, but the means and standard deviations are shared
#' among all drawn elements. The resulting tensor has size given by `size`.
#' 
#' @name torch_normal
#'
#' @export
NULL


#' Isfinite
#'
#' @section TEST :
#'
#' Returns a new tensor with boolean elements representing if each element is `Finite` or not.
#'
#'
#' @param self (Tensor) A tensor to check
#'
#' @name torch_isfinite
#'
#' @export
NULL


#' Isinf
#'
#' @section TEST :
#'
#' Returns a new tensor with boolean elements representing if each element is `+/-INF` or not.
#'
#'
#' @param self (Tensor) A tensor to check
#'
#' @name torch_isinf
#'
#' @export
NULL

#' Absolute
#'
#' @section absolute(input, *, out=None) -> Tensor:
#'
#' Alias for [torch_abs()]
#' @inheritParams torch_abs
#'
#' @name torch_absolute
#'
#' @export
NULL


#' View_as_real
#'
#' @section view_as_real(input) -> Tensor :
#'
#' Returns a view of `input` as a real tensor. For an input complex tensor of
#' `size` \eqn{m1, m2, \dots, mi}, this function returns a new
#' real tensor of size \eqn{m1, m2, \dots, mi, 2}, where the last dimension of size 2
#' represents the real and imaginary components of complex numbers.
#' 
#' @section Warning:
#' [torch_view_as_real()] is only supported for tensors with `complex dtypes`.
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_view_as_real
#'
#' @export
NULL

#' View_as_complex
#'
#' @section view_as_complex(input) -> Tensor :
#'
#' Returns a view of `input` as a complex tensor. For an input complex
#' tensor of `size` \eqn{m1, m2, \dots, mi, 2}, this function returns a
#' new complex tensor of `size` \eqn{m1, m2, \dots, mi} where the last
#' dimension of the input tensor is expected to represent the real and imaginary
#' components of complex numbers.
#' 
#' @section Warning:
#' [torch_view_as_complex] is only supported for tensors with
#' `torch_dtype` `torch_float64()` and `torch_float32()`.  The input is
#' expected to have the last dimension of `size` 2. In addition, the
#' tensor must have a `stride` of 1 for its last dimension. The strides of all
#' other dimensions must be even numbers.
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_view_as_complex
#'
#' @export
NULL


#' Sgn
#'
#' @section sgn(input, *, out=None) -> Tensor :
#'
#' For complex tensors, this function returns a new tensor whose elemants have the same angle as that of the
#' elements of `input` and absolute value 1. For a non-complex tensor, this function
#' returns the signs of the elements of `input` (see [`torch_sign`]).
#' 
#' \eqn{\mbox{out}_{i} = 0}, if \eqn{|{\mbox{{input}}_i}| == 0}
#' \eqn{\mbox{out}_{i} = \frac{{\mbox{{input}}_i}}{|{\mbox{{input}}_i}|}}, otherwise
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_sgn
#'
#' @export
NULL


#' Arccos
#'
#' @section arccos(input, *, out=None) -> Tensor :
#'
#' Alias for [torch_acos()].
#'
#' @inheritParams torch_acos
#' @name torch_arccos
#'
#' @export
NULL


#' Acosh
#'
#' @section acosh(input, *, out=None) -> Tensor :
#'
#' Returns a new tensor with the inverse hyperbolic cosine of the elements of `input`.
#' 
#' @note
#' The domain of the inverse hyperbolic cosine is `[1, inf)` and values outside this range
#' will be mapped to `NaN`, except for `+ INF` for which the output is mapped to `+ INF`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \cosh^{-1}(\mbox{input}_{i})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_acosh
#'
#' @export
NULL


#' Arccosh
#'
#' @section arccosh(input, *, out=None) -> Tensor :
#'
#' Alias for [torch_acosh()].
#'
#' @inheritParams torch_amax
#' @name torch_arccosh
#'
#' @export
NULL


#' Asinh
#'
#' @section asinh(input, *, out=None) -> Tensor :
#'
#' Returns a new tensor with the inverse hyperbolic sine of the elements of `input`.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \sinh^{-1}(\mbox{input}_{i})
#' }
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_asinh
#'
#' @export
NULL

#' Arcsinh
#'
#' @section arcsinh(input, *, out=None) -> Tensor :
#'
#' Alias for [torch_asinh()].
#'
#' @inheritParams torch_asinh
#' @name torch_arcsinh
#'
#' @export
NULL


#' Atanh
#'
#' @section atanh(input, *, out=None) -> Tensor :
#'
#' Returns a new tensor with the inverse hyperbolic tangent of the elements of `input`.
#' 
#' @note
#' The domain of the inverse hyperbolic tangent is `(-1, 1)` and values outside this range
#' will be mapped to `NaN`, except for the values `1` and `-1` for which the output is
#' mapped to `+/-INF` respectively.
#' 
#' \deqn{
#'     \mbox{out}_{i} = \tanh^{-1}(\mbox{input}_{i})
#' }
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_atanh
#'
#' @export
NULL

#' Arctanh
#'
#' @section arctanh(input, *, out=None) -> Tensor :
#'
#' Alias for [torch_atanh()].
#'
#' @inheritParams torch_atanh
#' @name torch_arctanh
#'
#' @export
NULL

#' Arcsin
#'
#' @section arcsin(input, *, out=None) -> Tensor :
#'
#' Alias for [torch_asin()].
#' 
#' @inheritParams torch_asin
#' @name torch_arcsin
#'
#' @export
NULL


#' Arctan
#'
#' @section arctan(input, *, out=None) -> Tensor :
#'
#' Alias for [torch_atan()].
#' 
#' @inheritParams torch_atan
#' @name torch_arctan
#'
#' @export
NULL


#' Atleast_1d
#'
#' Returns a 1-dimensional view of each input tensor with zero dimensions.
#' Input tensors with one or more dimensions are returned as-is.
#'
#' @param self (Tensor or list of Tensors) 
#'
#' @name torch_atleast_1d
#'
#' @export
NULL


#' Atleast_2d
#'
#' Returns a 2-dimensional view of each each input tensor with zero dimensions.
#' Input tensors with two or more dimensions are returned as-is.
#'
#' @param self (Tensor or list of Tensors) 
#'
#' @name torch_atleast_2d
#'
#' @export
NULL


#' Atleast_3d
#'
#' Returns a 3-dimensional view of each each input tensor with zero dimensions.
#' Input tensors with three or more dimensions are returned as-is.
#'
#' @param self (Tensor or list of Tensors) 
#'
#' @name torch_atleast_3d
#'
#' @export
NULL


#' Block_diag
#'
#' Create a block diagonal matrix from provided tensors.
#'
#' @param tensors (list of tensors) One or more tensors with 0, 1, or 2 
#'   dimensions.
#'
#' @name torch_block_diag
#'
#' @export
NULL

#' Unsafe_chunk
#'
#' @section unsafe_chunk(input, chunks, dim=0) -> List of Tensors :
#'
#' Works like [torch_chunk()] but without enforcing the autograd restrictions
#' on inplace modification of the outputs.
#' 
#' @inheritParams torch_chunk
#' 
#' @section Warning:
#' This function is safe to use as long as only the input, or only the outputs
#' are modified inplace after calling this function. It is user's
#' responsibility to ensure that is the case. If both the input and one or more
#' of the outputs are modified inplace, gradients computed by autograd will be
#' silently incorrect.
#'
#' @name torch_unsafe_chunk
#'
#' @export
NULL


#' Clip
#'
#' @section clip(input, min, max, *, out=None) -> Tensor :
#'
#' Alias for [torch_clamp()].
#'
#' @inheritParams torch_clamp
#' @name torch_clip
#'
#' @export
NULL


#' Complex
#'
#' @section complex(real, imag, *, out=None) -> Tensor :
#'
#' Constructs a complex tensor with its real part equal to `real` and its
#' imaginary part equal to `imag`.
#'
#' @param real (Tensor) The real part of the complex tensor. Must be float or double.
#' @param imag (Tensor) The imaginary part of the complex tensor. Must be same dtype 
#'   as `real`.
#'
#' @name torch_complex
#'
#' @export
NULL

#' Polar
#'
#' @section polar(abs, angle, *, out=None) -> Tensor :
#'
#' Constructs a complex tensor whose elements are Cartesian coordinates
#' corresponding to the polar coordinates with absolute value `abs` and angle
#' `angle`.
#' 
#' \deqn{
#'     \mbox{out} = \mbox{abs} \cdot \cos(\mbox{angle}) + \mbox{abs} \cdot \sin(\mbox{angle}) \cdot j
#' }
#'
#'
#' @param abs (Tensor) The absolute value the complex tensor. Must be float or 
#'   double.
#' @param angle (Tensor) The angle of the complex tensor. Must be same dtype as
#'   `abs`.
#'
#' @name torch_polar
#'
#' @export
NULL


#' Count_nonzero
#'
#' @section count_nonzero(input, dim=None) -> Tensor :
#'
#' Counts the number of non-zero values in the tensor `input` along the given `dim`.
#' If no dim is specified then all non-zeros in the tensor are counted.
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int or tuple of ints, optional) Dim or tuple of dims along which 
#'   to count non-zeros.
#'
#' @name torch_count_nonzero
#'
#' @export
NULL


#' Divide
#'
#' @section divide(input, other, *, out=None) -> Tensor :
#'
#' Alias for [torch_div()].
#'
#' @inheritParams torch_div
#' @name torch_divide
#'
#' @export
NULL


#' Vdot
#'
#' @section vdot(input, other, *, out=None) -> Tensor :
#'
#' Computes the dot product (inner product) of two tensors. The vdot(a, b) function
#' handles complex numbers differently than dot(a, b). If the first argument is complex,
#' the complex conjugate of the first argument is used for the calculation of the dot product.
#' 
#' @note This function does not broadcast .
#'
#' @param self (Tensor) first tensor in the dot product. Its conjugate is used 
#'   if it's complex.
#' @param other (Tensor) second tensor in the dot product.
#'
#' @name torch_vdot
#'
#' @export
NULL


#' Exp2
#'
#' @section exp2(input, *, out=None) -> Tensor :
#'
#' Computes the base two exponential function of `input`.
#' 
#' \deqn{
#'     y_{i} = 2^{x_{i}}
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_exp2
#'
#' @export
NULL


#' Gcd
#'
#' @section gcd(input, other, *, out=None) -> Tensor :
#'
#' Computes the element-wise greatest common divisor (GCD) of `input` and `other`.
#' 
#' Both `input` and `other` must have integer types.
#' 
#' @note This defines \eqn{gcd(0, 0) = 0}.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param other (Tensor) the second input tensor
#'
#' @name torch_gcd
#'
#' @export
NULL


#' Lcm
#'
#' @section lcm(input, other, *, out=None) -> Tensor :
#'
#' Computes the element-wise least common multiple (LCM) of `input` and `other`.
#' 
#' Both `input` and `other` must have integer types.
#' 
#' @note This defines \eqn{lcm(0, 0) = 0} and \eqn{lcm(0, a) = 0}.
#'
#' @param self (Tensor) the input tensor.
#' @param other (Tensor) the second input tensor
#'
#' @name torch_lcm
#'
#' @export
NULL


#' Kaiser_window
#'
#' @section kaiser_window(window_length, periodic=TRUE, beta=12.0, *, dtype=None, layout=torch.strided, device=None, requires_grad=FALSE) -> Tensor :
#'
#' Computes the Kaiser window with window length `window_length` and shape parameter `beta`.
#' 
#' Let I_0 be the zeroth order modified Bessel function of the first kind (see [torch_i0()]) and
#' `N = L - 1` if `periodic` is FALSE and `L` if `periodic` is TRUE,
#' where `L` is the `window_length`. This function computes:
#' 
#' \deqn{
#'     out_i = I_0 \left( \beta \sqrt{1 - \left( {\frac{i - N/2}{N/2}} \right) ^2 } \right) / I_0( \beta )
#' }
#' 
#' Calling `torch_kaiser_window(L, B, periodic=TRUE)` is equivalent to calling
#' `torch_kaiser_window(L + 1, B, periodic=FALSE)[:-1])`.
#' The `periodic` argument is intended as a helpful shorthand
#' to produce a periodic window as input to functions like [torch_stft()].
#' 
#' @note
#' If `window_length` is one, then the returned window is a single element 
#' tensor containing a one.
#'
#' @param window_length (int) length of the window.
#' @param periodic (bool, optional) If TRUE, returns a periodic window suitable for use in spectral analysis.        If FALSE, returns a symmetric window suitable for use in filter design.
#' @param beta (float, optional) shape parameter for the window.
#' @inheritParams torch_arange
#'
#' @name torch_kaiser_window
#'
#' @export
NULL


#' Isclose
#'
#' @section isclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor :
#'
#' Returns a new tensor with boolean elements representing if each element of
#' `input` is "close" to the corresponding element of `other`.
#' Closeness is defined as:
#' 
#' \deqn{
#'     \vert \mbox{input} - \mbox{other} \vert \leq \mbox{atol} + \mbox{rtol} \times \vert \mbox{other} \vert
#' }
#' 
#' where `input` and `other` are finite. Where `input`
#' and/or `other` are nonfinite they are close if and only if
#' they are equal, with NaNs being considered equal to each other when
#' `equal_nan` is TRUE.
#'
#'
#' @param self (Tensor) first tensor to compare
#' @param other (Tensor) second tensor to compare
#' @param atol (float, optional) absolute tolerance. Default: 1e-08
#' @param rtol (float, optional) relative tolerance. Default: 1e-05
#' @param equal_nan (bool, optional) if `TRUE`, then two `NaN` s will be 
#'   considered equal. Default: `FALSE`
#'
#' @name torch_isclose
#'
#' @export
NULL


#' Isreal
#'
#' @section isreal(input) -> Tensor :
#'
#' Returns a new tensor with boolean elements representing if each element of `input` is real-valued or not.
#' All real-valued types are considered real. Complex values are considered real when their imaginary part is 0.
#'
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_isreal
#'
#' @export
NULL


#' Is_nonzero
#'
#' @section is_nonzero(input) -> (bool) :
#'
#' Returns TRUE if the `input` is a single element tensor which is not equal to zero
#' after type conversions.
#' i.e. not equal to `torch_tensor(c(0))` or `torch_tensor(c(0))` or
#' `torch_tensor(c(FALSE))`.
#' Throws a `RuntimeError` if `torch_numel() != 1` (even in case
#' of sparse tensors).
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_is_nonzero
#'
#' @export
NULL


#' Logaddexp
#'
#' @section logaddexp(input, other, *, out=None) -> Tensor :
#'
#' Logarithm of the sum of exponentiations of the inputs.
#' 
#' Calculates pointwise \eqn{\log\left(e^x + e^y\right)}. This function is useful
#' in statistics where the calculated probabilities of events may be so small as to
#' exceed the range of normal floating point numbers. In such cases the logarithm
#' of the calculated probability is stored. This function allows adding
#' probabilities stored in such a fashion.
#' 
#' This op should be disambiguated with [torch_logsumexp()] which performs a
#' reduction on a single tensor.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param other (Tensor) the second input tensor
#'
#' @name torch_logaddexp
#'
#' @export
NULL


#' Logaddexp2
#'
#' @section logaddexp2(input, other, *, out=None) -> Tensor :
#'
#' Logarithm of the sum of exponentiations of the inputs in base-2.
#' 
#' Calculates pointwise \eqn{\log_2\left(2^x + 2^y\right)}. See
#' [torch_logaddexp()] for more details.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param other (Tensor) the second input tensor
#'
#' @name torch_logaddexp2
#'
#' @export
NULL


#' Logcumsumexp
#'
#' @section logcumsumexp(input, dim, *, out=None) -> Tensor :
#'
#' Returns the logarithm of the cumulative summation of the exponentiation of
#' elements of `input` in the dimension `dim`.
#' 
#' For summation index \eqn{j} given by `dim` and other indices \eqn{i}, the result is
#' 
#' \deqn{
#'         \mbox{logcumsumexp}(x)_{ij} = \log \sum\limits_{j=0}^{i} \exp(x_{ij})
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int) the dimension to do the operation over
#'
#' @name torch_logcumsumexp
#'
#' @export
NULL


#' Matrix_exp
#'
#' @section matrix_power(input) -> Tensor :
#'
#' Returns the matrix exponential. Supports batched input.
#' For a matrix `A`, the matrix exponential is defined as
#' 
#' \deqn{
#'     \exp^A = \sum_{k=0}^\infty A^k / k!.
#' }
#' 
#' The implementation is based on:
#' Bader, P.; Blanes, S.; Casas, F.
#' Computing the Matrix Exponential with an Optimized Taylor Polynomial Approximation.
#' Mathematics 2019, 7, 1174.
#'
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_matrix_exp
#'
#' @export
NULL


#' Amax
#'
#' @section amax(input, dim, keepdim=FALSE, *, out=None) -> Tensor :
#'
#' Returns the maximum value of each slice of the `input` tensor in the given
#' dimension(s) `dim`.
#' 
#' @note
#' The difference between `max`/`min` and `amax`/`amin` is:
#' - `amax`/`amin` supports reducing on multiple dimensions,
#' - `amax`/`amin` does not return indices,
#' - `amax`/`amin` evenly distributes gradient between equal values,
#'    while `max(dim)`/`min(dim)` propagates gradient only to a single
#'    index in the source tensor.
#' 
#' If `keepdim is `TRUE`, the output tensors are of the same size
#' as `input` except in the dimension(s) `dim` where they are of size 1.
#' Otherwise, `dim`s are squeezed (see [torch_squeeze()]), resulting
#' in the output tensors having fewer dimension than `input`.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#'
#' @name torch_amax
#'
#' @export
NULL


#' Amin
#'
#' @section amin(input, dim, keepdim=FALSE, *, out=None) -> Tensor :
#'
#' Returns the minimum value of each slice of the `input` tensor in the given
#' dimension(s) `dim`.
#' 
#' @note
#' The difference between `max`/`min` and `amax`/`amin` is:
#' - `amax`/`amin` supports reducing on multiple dimensions,
#' - `amax`/`amin` does not return indices,
#' - `amax`/`amin` evenly distributes gradient between equal values,
#'    while `max(dim)`/`min(dim)` propagates gradient only to a single
#'    index in the source tensor.
#' 
#' If `keepdim` is `TRUE`, the output tensors are of the same size as
#' `input` except in the dimension(s) `dim` where they are of size 1.
#' Otherwise, `dim`s are squeezed (see [torch_squeeze()]), resulting in
#' the output tensors having fewer dimensions than `input`.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#'
#' @name torch_amin
#'
#' @export
NULL


#' Multiply
#'
#' @section multiply(input, other, *, out=None) :
#'
#' Alias for [torch_mul()].
#'
#' @inheritParams torch_mul
#' @name torch_multiply
#'
#' @export
NULL


#' Movedim
#'
#' @section movedim(input, source, destination) -> Tensor :
#'
#' Moves the dimension(s) of `input` at the position(s) in `source`
#' to the position(s) in `destination`.
#' 
#' Other dimensions of `input` that are not explicitly moved remain in
#' their original order and appear at the positions not specified in `destination`.
#'
#' @param self (Tensor) the input tensor.
#' @param source (int or tuple of ints) Original positions of the dims to move. These must be unique.
#' @param destination (int or tuple of ints) Destination positions for each of the original dims. These must also be unique.
#'
#' @name torch_movedim
#'
#' @export
NULL


#' Channel_shuffle
#'
#' @section Divide the channels in a tensor of shape :math:`(*, C , H, W)` :
#'
#' Divide the channels in a tensor of shape \eqn{(*, C , H, W)}
#' into g groups and rearrange them as \eqn{(*, C \frac g, g, H, W)},
#' while keeping the original tensor shape.
#'
#' @param self (Tensor) the input tensor
#' @param groups (int) number of groups to divide channels in and rearrange.
#'
#' @name torch_channel_shuffle
#'
#' @export
NULL


#' Rad2deg
#'
#' @section rad2deg(input, *, out=None) -> Tensor :
#'
#' Returns a new tensor with each of the elements of `input`
#' converted from angles in radians to degrees.
#'
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_rad2deg
#'
#' @export
NULL


#' Deg2rad
#'
#' @section deg2rad(input, *, out=None) -> Tensor :
#'
#' Returns a new tensor with each of the elements of `input`
#' converted from angles in degrees to radians.
#'
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_deg2rad
#'
#' @export
NULL


#' Negative
#'
#' @section negative(input, *, out=None) -> Tensor :
#'
#' Alias for [torch_neg()]
#' 
#' @inheritParams torch_neg
#' @name torch_negative
#'
#' @export
NULL


#' Logit
#'
#' @section logit(input, eps=None, *, out=None) -> Tensor :
#'
#' Returns a new tensor with the logit of the elements of `input`.
#' `input` is clamped to `[eps, 1 - eps]` when eps is not None.
#' When eps is None and `input` < 0 or `input` > 1, the function will yields NaN.
#' 
#' \deqn{
#'     y_{i} = \ln(\frac{z_{i}}{1 - z_{i}}) \\
#'     z_{i} = \begin{array}{ll}
#'         x_{i} & \mbox{if eps is None} \\
#'         \mbox{eps} & \mbox{if } x_{i} < \mbox{eps} \\
#'         x_{i} & \mbox{if } \mbox{eps} \leq x_{i} \leq 1 - \mbox{eps} \\
#'         1 - \mbox{eps} & \mbox{if } x_{i} > 1 - \mbox{eps}
#'     \end{array}
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' @param eps (float, optional) the epsilon for input clamp bound. Default: `None`
#'
#' @name torch_logit
#'
#' @export
NULL


#' Unsafe_split
#'
#' @section unsafe_split(tensor, split_size_or_sections, dim=0) -> List of Tensors :
#'
#' Works like [torch_split()] but without enforcing the autograd restrictions
#' on inplace modification of the outputs.
#' 
#' @section Warning:
#' This function is safe to use as long as only the input, or only the outputs
#' are modified inplace after calling this function. It is user's
#' responsibility to ensure that is the case. If both the input and one or more
#' of the outputs are modified inplace, gradients computed by autograd will be
#' silently incorrect.
#' 
#' @inheritParams torch_split
#' @name torch_unsafe_split
#'
#' @export
NULL


#' Hstack
#'
#' @section hstack(tensors, *, out=None) -> Tensor :
#'
#' Stack tensors in sequence horizontally (column wise).
#' 
#' This is equivalent to concatenation along the first axis for 1-D tensors, and 
#' along the second axis for all other tensors.
#'
#' @param tensors (sequence of Tensors) sequence of tensors to concatenate
#'
#' @name torch_hstack
#'
#' @export
NULL


#' Vstack
#'
#' @section vstack(tensors, *, out=None) -> Tensor :
#'
#' Stack tensors in sequence vertically (row wise).
#' 
#' This is equivalent to concatenation along the first axis after all 1-D tensors 
#' have been reshaped by [torch_atleast_2d()].
#'
#' @param tensors (sequence of Tensors) sequence of tensors to concatenate
#'
#' @name torch_vstack
#'
#' @export
NULL


#' Dstack
#'
#' @section dstack(tensors, *, out=None) -> Tensor :
#'
#' Stack tensors in sequence depthwise (along third axis).
#' 
#' This is equivalent to concatenation along the third axis after 1-D and 2-D 
#' tensors have been reshaped by [torch_atleast_3d()].
#'
#' @param tensors (sequence of Tensors) sequence of tensors to concatenate
#'
#' @name torch_dstack
#'
#' @export
NULL


#' Istft
#'
#' Inverse short time Fourier Transform. This is expected to be the inverse of [torch_stft()].
#' 
#' It has the same parameters (+ additional optional parameter of `length`) and it should return the
#' least squares estimation of the original signal. The algorithm will check using the NOLA 
#' condition (nonzero overlap).
#' 
#' Important consideration in the parameters `window` and `center` so that the envelop
#' created by the summation of all the windows is never zero at certain point in time. Specifically,
#' \eqn{\sum_{t=-\infty}^{\infty} |w|^2(n-t\times hop_length) \neq 0}.
#' 
#' Since [torch_stft()] discards elements at the end of the signal if they do not fit in a frame,
#' `istft` may return a shorter signal than the original signal (can occur if `center` is FALSE
#' since the signal isn't padded).
#' 
#' If `center` is `TRUE`, then there will be padding e.g. `'constant'`, `'reflect'`, etc.
#' Left padding can be trimmed off exactly because they can be calculated but right 
#' padding cannot be calculated without additional information.
#' 
#' Example: Suppose the last window is:
#' `[c(17, 18, 0, 0, 0)` vs `c(18, 0, 0, 0, 0)`
#' 
#' The `n_fft`, `hop_length`, `win_length` are all the same which prevents the calculation
#' of right padding. These additional values could be zeros or a reflection of the signal so providing
#' `length` could be useful. If `length` is `None` then padding will be aggressively removed
#' (some loss of signal).
#' 
#' D. W. Griffin and J. S. Lim, "Signal estimation from modified short-time Fourier transform,"
#' IEEE Trans. ASSP, vol.32, no.2, pp.236-243, Apr. 1984.
#'
#' @param self (Tensor) The input tensor. Expected to be output of [torch_stft()], 
#'   can either be complex (`channel`, `fft_size`, `n_frame`), or real 
#'   (`channel`, `fft_size`, `n_frame`, 2) where the `channel` dimension is 
#'   optional.
#' @param n_fft (int) Size of Fourier transform
#' @param hop_length (Optional`[int]`) The distance between neighboring sliding window frames.
#'   (Default: `n_fft %% 4`)
#' @param win_length (Optional`[int]`) The size of window frame and STFT filter. 
#'   (Default: `n_fft`)
#' @param window (Optional(torch.Tensor)) The optional window function.
#'   (Default: `torch_ones(win_length)`)
#' @param center (bool) Whether `input` was padded on both sides so that the 
#'   \eqn{t}-th frame is centered at time \eqn{t \times \mbox{hop\_length}}.
#'   (Default: `TRUE`)
#' @param normalized (bool) Whether the STFT was normalized. (Default: `FALSE`)
#' @param onesided (Optional(bool)) Whether the STFT was onesided. 
#'   (Default: `TRUE` if `n_fft != fft_size` in the input size)
#' @param length (Optional(int)]) The amount to trim the signal by (i.e. the 
#'   original signal length). (Default: whole signal)
#' @param return_complex (Optional(bool)) Whether the output should be complex, 
#'   or if the input should be assumed to derive from a real signal and window. 
#'   Note that this is incompatible with `onesided=TRUE`. (Default: `FALSE`)
#'
#' @name torch_istft
#'
#' @export
NULL


#' Nansum
#'
#' @section nansum(input, *, dtype=None) -> Tensor :
#'
#' Returns the sum of all elements, treating Not a Numbers (NaNs) as zero.
#'
#' @section nansum(input, dim, keepdim=FALSE, *, dtype=None) -> Tensor :
#'
#' Returns the sum of each row of the `input` tensor in the given
#' dimension `dim`, treating Not a Numbers (NaNs) as zero.
#' If `dim` is a list of dimensions, reduce over all of them.
#' 
#' If `keepdim` is `TRUE`, the output tensor is of the same size
#' as `input` except in the dimension(s) `dim` where it is of size 1.
#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
#'
#' @param self (Tensor) the input tensor.
#' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#' @param dtype the desired data type of returned tensor. If specified, the 
#'   input tensor is casted to dtype before the operation is performed. This is 
#'   useful for preventing data type overflows. Default: `NULL`.
#'
#' @name torch_nansum
#'
#' @export
NULL

#' Fliplr
#'
#' @section fliplr(input) -> Tensor :
#'
#' Flip array in the left/right direction, returning a new tensor.
#' 
#' Flip the entries in each row in the left/right direction.
#' Columns are preserved, but appear in a different order than before.
#' 
#' @note
#' Equivalent to `input[,-1]`. Requires the array to be at least 2-D.
#'
#'
#' @param self (Tensor) Must be at least 2-dimensional.
#'
#' @name torch_fliplr
#'
#' @export
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#' Flipud
#'
#' @section flipud(input) -> Tensor :
#'
#' Flip array in the up/down direction, returning a new tensor.
#' 
#' Flip the entries in each column in the up/down direction.
#' Rows are preserved, but appear in a different order than before.
#' 
#' @note
#' Equivalent to `input[-1,]`. Requires the array to be at least 1-D.
#'
#'
#' @param self (Tensor) Must be at least 1-dimensional.
#'
#' @name torch_flipud
#'
#' @export
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#' Fix
#'
#' @section fix(input, *, out=None) -> Tensor :
#'
#' Alias for [torch_trunc()]
#'
#' @inheritParams torch_trunc
#' @name torch_fix
#'
#' @export
NULL


#' Vander
#'
#' @section vander(x, N=None, increasing=FALSE) -> Tensor :
#'
#' Generates a Vandermonde matrix.
#' 
#' The columns of the output matrix are elementwise powers of the input vector 
#' \eqn{x^{(N-1)}, x^{(N-2)}, ..., x^0}.
#' If increasing is TRUE, the order of the columns is reversed 
#' \eqn{x^0, x^1, ..., x^{(N-1)}}. Such a
#' matrix with a geometric progression in each row is 
#' named for Alexandre-Theophile Vandermonde.
#'
#'
#' @param x (Tensor) 1-D input tensor.
#' @param N (int, optional) Number of columns in the output. If N is not specified,        
#'   a square array is returned \eqn{(N = len(x))}.
#' @param increasing (bool, optional) Order of the powers of the columns. If TRUE,        
#'   the powers increase from left to right, if FALSE (the default) they are reversed.
#'
#' @name torch_vander
#'
#' @export
NULL


#' Clone
#'
#' @section clone(input, *, memory_format=torch.preserve_format) -> Tensor :
#'
#' Returns a copy of `input`.
#' 
#' @note
#' 
#' This function is differentiable, so gradients will flow back from the
#' result of this operation to `input`. To create a tensor without an
#' autograd relationship to `input` see `Tensor$detach`.
#'
#' @param self (Tensor) the input tensor.
#' @param memory_format a torch memory format. see [torch_preserve_format()].
#' 
#' @name torch_clone
#'
#' @export
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#' Sub
#'
#' @section sub(input, other, *, alpha=1, out=None) -> Tensor :
#'
#' Subtracts `other`, scaled by `alpha`, from `input`.
#' 
#' \deqn{
#'     \mbox{{out}}_i = \mbox{{input}}_i - \mbox{{alpha}} \times \mbox{{other}}_i
#' }
#' 
#' Supports broadcasting to a common shape ,
#' type promotion , and integer, float, and complex inputs.
#'
#' @param self (Tensor) the input tensor.
#' @param other (Tensor or Scalar) the tensor or scalar to subtract from `input`
#' @param alpha the scalar multiplier for other
#' 
#' @name torch_sub
#'
#' @export
NULL


#' Subtract
#'
#' @section subtract(input, other, *, alpha=1, out=None) -> Tensor :
#'
#' Alias for [torch_sub()].
#'
#' @inheritParams torch_sub
#' @name torch_subtract
#'
#' @export
NULL


#' Heaviside
#'
#' @section heaviside(input, values, *, out=None) -> Tensor :
#'
#' Computes the Heaviside step function for each element in `input`.
#' The Heaviside step function is defined as:
#' 
#' \deqn{
#' \mbox{{heaviside}}(input, values) = \begin{array}{ll}
#'  0, & \mbox{if input < 0}\\
#'  values, & \mbox{if input == 0}\\
#'  1, & \mbox{if input > 0}
#'  \end{array}
#' }
#'
#'
#' @param self (Tensor) the input tensor.
#' @param values (Tensor) The values to use where `input` is zero.
#'
#' @name torch_heaviside
#'
#' @export
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#' Dequantize
#'
#' @section dequantize(tensor) -> Tensor :
#'
#' Returns an fp32 Tensor by dequantizing a quantized Tensor
#'
#' @section dequantize(tensors) -> sequence of Tensors :
#'
#' Given a list of quantized Tensors, dequantize them and return a list of fp32 Tensors
#'
#' @param tensor (Tensor) A quantized Tensor or a list oof quantized tensors
#'
#' @name torch_dequantize
#'
#' @export
NULL


#' Not_equal
#'
#' @section not_equal(input, other, *, out=None) -> Tensor :
#'
#' Alias for [torch_ne()].
#'
#' @inheritParams torch_ne
#' @name torch_not_equal
#'
#' @export
NULL


#' Greater_equal
#'
#' @section greater_equal(input, other, *, out=None) -> Tensor :
#'
#' Alias for [torch_ge()].
#'
#' @inheritParams torch_ge
#' @name torch_greater_equal
#'
#' @export
NULL


#' Less_equal
#'
#' @section less_equal(input, other, *, out=None) -> Tensor :
#'
#' Alias for [torch_le()].
#'
#' @inheritParams torch_le
#' @name torch_less_equal
#'
#' @export
NULL


#' Greater
#'
#' @section greater(input, other, *, out=None) -> Tensor :
#'
#' Alias for [torch_gt()].
#'
#' @inheritParams torch_gt
#' @name torch_greater
#'
#' @export
NULL


#' Less
#'
#' @section less(input, other, *, out=None) -> Tensor :
#'
#' Alias for [torch_lt()].
#'
#' @inheritParams torch_lt
#' @name torch_less
#'
#' @export
NULL


#' I0
#'
#' @section i0(input, *, out=None) -> Tensor :
#'
#' Computes the zeroth order modified Bessel function of the first kind for each element of `input`.
#' 
#' \deqn{
#' \mbox{out}_{i} = I_0(\mbox{input}_{i}) = \sum_{k=0}^{\infty} \frac{(\mbox{input}_{i}^2/4)^k}{(k!)^2}
#' }
#'
#' @param self (Tensor) the input tensor
#'
#' @name torch_i0
#'
#' @export
NULL


#' Signbit
#'
#' @section signbit(input, *, out=None) -> Tensor :
#'
#' Tests if each element of `input` has its sign bit set (is less than zero) or not.
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_signbit
#'
#' @export
NULL


#' Hypot
#'
#' @section hypot(input, other, *, out=None) -> Tensor :
#'
#' Given the legs of a right triangle, return its hypotenuse.
#' 
#' \deqn{
#' \mbox{out}_{i} = \sqrt{\mbox{input}_{i}^{2} + \mbox{other}_{i}^{2}}
#' }
#' 
#' The shapes of `input` and `other` must be
#' broadcastable .
#'
#'
#' @param self (Tensor) the first input tensor
#' @param other (Tensor) the second input tensor
#'
#' @name torch_hypot
#'
#' @export
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#' Nextafter
#'
#' @section nextafter(input, other, *, out=None) -> Tensor :
#'
#' Return the next floating-point value after `input` towards `other`, elementwise.
#' 
#' The shapes of `input` and `other` must be
#' broadcastable .
#'
#'
#' @param self (Tensor) the first input tensor
#' @param other (Tensor) the second input tensor
#'
#' @name torch_nextafter
#'
#' @export
NULL


#' Maximum
#'
#' @section maximum(input, other, *, out=None) -> Tensor :
#'
#' Computes the element-wise maximum of `input` and `other`.
#' 
#' @note
#' If one of the elements being compared is a NaN, then that element is returned.
#' [torch_maximum()] is not supported for tensors with complex dtypes.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param other (Tensor) the second input tensor
#'
#' @name torch_maximum
#'
#' @export
NULL


#' Minimum
#'
#' @section minimum(input, other, *, out=None) -> Tensor :
#'
#' Computes the element-wise minimum of `input` and `other`.
#' 
#' @note
#' If one of the elements being compared is a NaN, then that element is returned.
#' [torch_minimum()] is not supported for tensors with complex dtypes.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param other (Tensor) the second input tensor
#'
#' @name torch_minimum
#'
#' @export
NULL


#' Quantile
#'
#' @section quantile(input, q) -> Tensor :
#'
#' Returns the q-th quantiles of all elements in the `input` tensor, doing a linear
#' interpolation when the q-th quantile lies between two data points.
#'
#' @section quantile(input, q, dim=None, keepdim=FALSE, *, out=None) -> Tensor :
#'
#' Returns the q-th quantiles of each row of the `input` tensor along the dimension
#' `dim`, doing a linear interpolation when the q-th quantile lies between two
#' data points. By default, `dim` is `None` resulting in the `input` tensor
#' being flattened before computation.
#' 
#' If `keepdim` is `TRUE`, the output dimensions are of the same size as `input`
#' except in the dimensions being reduced (`dim` or all if `dim` is `NULL`) where they
#' have size 1. Otherwise, the dimensions being reduced are squeezed (see [`torch_squeeze`]).
#' If `q` is a 1D tensor, an extra dimension is prepended to the output tensor with the same
#' size as `q` which represents the quantiles.
#'
#'
#' @param self (Tensor) the input tensor.
#' @param q (float or Tensor) a scalar or 1D tensor of quantile values in the range `[0, 1]`
#' @param dim (int) the dimension to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#' @inheritParams torch_nanquantile
#'
#' @name torch_quantile
#'
#' @export
NULL


#' Nanquantile
#'
#' @section nanquantile(input, q, dim=None, keepdim=FALSE, *, out=None) -> Tensor :
#'
#' This is a variant of [torch_quantile()] that "ignores" `NaN` values,
#' computing the quantiles `q` as if `NaN` values in `input` did
#' not exist. If all values in a reduced row are `NaN` then the quantiles for
#' that reduction will be `NaN`. See the documentation for [torch_quantile()].
#'
#'
#' @param self (Tensor) the input tensor.
#' @param q (float or Tensor) a scalar or 1D tensor of quantile values in the range `[0, 1]`
#' @param dim (int) the dimension to reduce.
#' @param keepdim (bool) whether the output tensor has `dim` retained or not.
#' @param interpolation The interpolation method.
#'
#' @name torch_nanquantile
#'
#' @export
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#' Bucketize
#'
#' @section bucketize(input, boundaries, *, out_int32=FALSE, right=FALSE, out=None) -> Tensor :
#'
#' Returns the indices of the buckets to which each value in the `input` belongs, where the
#' boundaries of the buckets are set by `boundaries`. Return a new tensor with the same size
#' as `input`. If `right` is FALSE (default), then the left boundary is closed. 
#'
#' @param self (Tensor or Scalar) N-D tensor or a Scalar containing the search value(s).
#' @param boundaries (Tensor) 1-D tensor, must contain a monotonically increasing sequence.
#' @param out_int32 (bool, optional) – indicate the output data type. [torch_int32()] 
#'  if True, [torch_int64()] otherwise. Default value is FALSE, i.e. default output 
#'  data type is [torch_int64()].
#' @param right (bool, optional) – if False, return the first suitable location 
#'  that is found. If True, return the last such index. If no suitable index found, 
#'  return 0 for non-numerical value (eg. nan, inf) or the size of boundaries 
#'  (one pass the last index). In other words, if False, gets the lower bound index 
#'  for each value in input from boundaries. If True, gets the upper bound index 
#'  instead. Default value is False.
#'
#' @name torch_bucketize
#'
#' @export
NULL


#' Searchsorted
#'
#' @section searchsorted(sorted_sequence, values, *, out_int32=FALSE, right=FALSE, out=None) -> Tensor :
#'
#' Find the indices from the *innermost* dimension of `sorted_sequence` such that, if the
#' corresponding values in `values` were inserted before the indices, the order of the
#' corresponding *innermost* dimension within `sorted_sequence` would be preserved.
#' Return a new tensor with the same size as `values`. If `right` is FALSE (default),
#' then the left boundary of `sorted_sequence` is closed.
#'
#' @param sorted_sequence (Tensor) N-D or 1-D tensor, containing monotonically increasing 
#'   sequence on the *innermost* dimension.
#' @param self (Tensor or Scalar) N-D tensor or a Scalar containing the search value(s).
#' @param side the same as right but preferred. “left” corresponds to `FALSE` for right 
#'   and “right” corresponds to `TRUE` for right. It will error if this is set to 
#'   “left” while right is `TRUE`.
#' @param sorter if provided, a tensor matching the shape of the unsorted `sorted_sequence` 
#'   containing a sequence of indices that sort it in the ascending order on the 
#'   innermost dimension.
#'   
#' @inheritParams torch_bucketize
#' @name torch_searchsorted
#'
#' @export
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#' Isposinf
#'
#' @section isposinf(input, *, out=None) -> Tensor :
#'
#' Tests if each element of `input` is positive infinity or not.
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_isposinf
#'
#' @export
NULL


#' Isneginf
#'
#' @section isneginf(input, *, out=None) -> Tensor :
#'
#' Tests if each element of `input` is negative infinity or not.
#'
#' @param self (Tensor) the input tensor.
#'
#' @name torch_isneginf
#'
#' @export
NULL


#' Outer
#'
#' @section outer(input, vec2, *, out=None) -> Tensor :
#'
#' Outer product of `input` and `vec2`.
#' If `input` is a vector of size \eqn{n} and `vec2` is a vector of
#' size \eqn{m}, then `out` must be a matrix of size \eqn{(n \times m)}.
#' 
#' @note This function does not broadcast.
#'
#' @param self (Tensor) 1-D input vector
#' @param vec2 (Tensor) 1-D input vector
#'
#' @name torch_outer
#'
#' @export
NULL

#' Computes the n-th forward difference along the given dimension.
#' 
#' The first-order differences are given by `out[i] = input[i + 1] - input[i]`. 
#' Higher-order differences are calculated by using [torch_diff()] recursively.
#'
#' @note Only n = 1 is currently supported
#' 
#' @param self the tensor to compute the differences on
#' @param n the number of times to recursively compute the difference
#' @param dim the dimension to compute the difference along. Default is the last dimension.
#' @param prepend values to prepend to input along dim before computing the 
#'  difference. Their dimensions must be equivalent to that of input, and their 
#'  shapes must match input’s shape except on dim.
#' @param append values to append to input along dim before computing the 
#'  difference. Their dimensions must be equivalent to that of input, and their 
#'  shapes must match input’s shape except on dim.
#'  
#' @examples
#' a <- torch_tensor(c(1,2,3))
#' torch_diff(a)
#' 
#' b <- torch_tensor(c(4, 5))
#' torch_diff(a, append = b)
#' 
#' c <- torch_tensor(rbind(c(1,2,3), c(3,4,5)))
#' torch_diff(c, dim = 1)
#' torch_diff(c, dim = 2) 
#' 
#' @name torch_diff
#' @export
NULL

#' Lu_unpack
#'
#' @section lu_unpack(LU_data, LU_pivots, unpack_data = TRUE, unpack_pivots=TRUE) -> Tensor :
#'
#' Unpacks the data and pivots from a LU factorization of a tensor into tensors `L` and `U` and
#' a permutation tensor `P` such that `LU_data_and_pivots <- torch_lu(P$matmul(L)$matmul(U))`.
#' Returns a list of tensors as `list(the P tensor (permutation matrix), the L tensor, the U tensor)`
#' 
#' @param LU_data (Tensor) – the packed LU factorization data
#' @param LU_pivots (Tensor) – the packed LU factorization pivots
#' @param unpack_data (logical) – flag indicating if the data should be unpacked. If FALSE, then the returned L and U are NULL Default: TRUE
#' @param unpack_pivots (logical) – flag indicating if the pivots should be unpacked into a permutation matrix P. If FALSE, then the returned P is None. Default: TRUE
#'
#' @name torch_lu_unpack
#' @export
NULL

#' Kronecker product
#'
#' Computes the Kronecker product of `self` and `other`.
#' 
#' @param self (`Tensor`) input Tensor
#' @param other (`Tensor`) other tensor.
#' 
#' @name torch_kron
#' @export
NULL