#' @title Augment data with information from a(n) <%= class %> object
#'
#' @description Augment accepts a model object and a dataset and adds
#' information about each observation in the dataset. Most commonly, this
#' includes predicted values in the `.fitted` column, residuals in the
#' `.resid` column, and standard errors for the fitted values in a `.se.fit`
#' column. New columns always begin with a `.` prefix to avoid overwriting
#' columns in the original dataset.
#'
#' Users may pass data to augment via either the `data` argument or the
#' `newdata` argument. If the user passes data to the `data` argument,
#' it **must** be exactly the data that was used to fit the model
#' object. Pass datasets to `newdata` to augment data that was not used
#' during model fitting. This still requires that at least all predictor
#' variable columns used to fit the model are present. If the original outcome
#' variable used to fit the model is not included in `newdata`, then no
#' `.resid` column will be included in the output.
#'
#' Augment will often behave differently depending on whether `data` or
#' `newdata` is given. This is because there is often information
#' associated with training observations (such as influences or related)
#' measures that is not meaningfully defined for new observations.
#'
#' For convenience, many augment methods provide default `data` arguments,
#' so that `augment(fit)` will return the augmented training data. In these
#' cases, augment tries to reconstruct the original data based on the model
#' object with varying degrees of success.
#'
#' The augmented dataset is always returned as a [tibble::tibble] with the
#' **same number of rows** as the passed dataset. This means that the passed
#' data must be coercible to a tibble. If a predictor enters the model as part
#' of a matrix of covariates, such as when the model formula uses
#' [splines::ns()], [stats::poly()], or [survival::Surv()], it is represented
#' as a matrix column.
#'
#' We are in the process of defining behaviors for models fit with various
#' `na.action` arguments, but make no guarantees about behavior when data is
#' missing at this time.
#'
#' @md
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