class_docstr: Base class for random projections.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
docstr: Generate a sparse random projection matrix.
  
  Parameters
  ----------
  X : {ndarray, sparse matrix} of shape (n_samples, n_features)
      Training set: only the shape is used to find optimal random
      matrix dimensions based on the theory referenced in the
      afore mentioned papers.
  
  y : Ignored
      Not used, present here for API consistency by convention.
  
  Returns
  -------
  self : object
      BaseRandomProjection class instance.
code: def fit(self, X, y=None):
      """Generate a sparse random projection matrix.
  
      Parameters
      ----------
      X : {ndarray, sparse matrix} of shape (n_samples, n_features)
          Training set: only the shape is used to find optimal random
          matrix dimensions based on the theory referenced in the
          afore mentioned papers.
  
      y : Ignored
          Not used, present here for API consistency by convention.
  
      Returns
      -------
      self : object
          BaseRandomProjection class instance.
      """
      X = self._validate_data(
          X,
          accept_sparse=["csr", "csc"],
          dtype=[np.float64, np.float32],
      )
  
      n_samples, n_features = X.shape
  
      if self.n_components == "auto":
          self.n_components_ = johnson_lindenstrauss_min_dim(
              n_samples=n_samples, eps=self.eps
          )
  
          if self.n_components_ <= 0:
              raise ValueError(
                  "eps=%f and n_samples=%d lead to a target dimension of "
                  "%d which is invalid"
                  % (self.eps, n_samples, self.n_components_)
              )
  
          elif self.n_components_ > n_features:
              raise ValueError(
                  "eps=%f and n_samples=%d lead to a target dimension of "
                  "%d which is larger than the original space with "
                  "n_features=%d"
                  % (
                      self.eps,
                      n_samples,
                      self.n_components_,
                      n_features,
                  )
              )
      else:
          if self.n_components > n_features:
              warnings.warn(
                  "The number of components is higher than the number of"
                  " features: n_features < n_components (%s < %s)."
                  "The dimensionality of the problem will not be reduced."
                  % (n_features, self.n_components),
                  DataDimensionalityWarning,
              )
  
          self.n_components_ = self.n_components
  
      # Generate a projection matrix of size [n_components, n_features]
      self.components_ = self._make_random_matrix(
          self.n_components_, n_features
      ).astype(X.dtype, copy=False)
  
      if self.compute_inverse_components:
          self.inverse_components_ = self._compute_inverse_components()
  
      # Required by ClassNamePrefixFeaturesOutMixin.get_feature_names_out.
      self._n_features_out = self.n_components
  
      return self
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral, Real
  import numpy
  import scipy.sparse
  from scipy import linalg
  from .base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
  from .exceptions import DataDimensionalityWarning
  from .utils import check_random_state
  from .utils._param_validation import Interval, StrOptions, validate_params
  from .utils.extmath import safe_sparse_dot
  from .utils.random import sample_without_replacement
  from .utils.validation import check_array, check_is_fitted
label_desc: Use image node_id 4 for calling the BaseRandomProjection obj's underlying member method code with example usage: obj.fit(X, y) and returns: self