sampler: Creates a new Sampler
In torch: Tensors and Neural Networks with 'GPU' Acceleration

sampler

R Documentation

Creates a new Sampler

Samplers can be used with dataloader() when creating batches from a torch dataset().

sampler(
  name = NULL,
  inherit = Sampler,
  ...,
  private = NULL,
  active = NULL,
  parent_env = parent.frame()
)

`name`	(optional) name of the sampler
`inherit`	(optional) you can inherit from other samplers to re-use some methods.
`...`	Pass any number of fields or methods. You should at least define the `initialize` and `step` methods. See the examples section.
`private`	(optional) a list of private methods for the sampler
`active`	(optional) a list of active methods for the sampler.
`parent_env`	used to capture the right environment to define the class. The default is fine for most situations.

A sampler must implement the .iter and .length() methods.

initialize takes in a data_source. In general this is a dataset().
.iter returns a function that returns a dataset index everytime it's called.
.length returns the maximum number of samples that can be retrieved from that sampler.