code: def _gaussian_random_matrix(
      n_components, n_features, random_state=None
  ):
      """Generate a dense Gaussian random matrix.
  
      The components of the random matrix are drawn from
  
          N(0, 1.0 / n_components).
  
      Read more in the :ref:`User Guide <gaussian_random_matrix>`.
  
      Parameters
      ----------
      n_components : int,
          Dimensionality of the target projection space.
  
      n_features : int,
          Dimensionality of the original source space.
  
      random_state : int, RandomState instance or None, default=None
          Controls the pseudo random number generator used to generate the matrix
          at fit time.
          Pass an int for reproducible output across multiple function calls.
          See :term:`Glossary <random_state>`.
  
      Returns
      -------
      components : ndarray of shape (n_components, n_features)
          The generated Gaussian random matrix.
  
      See Also
      --------
      GaussianRandomProjection
      """
      _check_input_size(n_components, n_features)
      rng = check_random_state(random_state)
      components = rng.normal(
          loc=0.0,
          scale=1.0 / np.sqrt(n_components),
          size=(n_components, n_features),
      )
      return components
  
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 a global function with example usage: _gaussian_random_matrix(n_components, n_features, random_state) and returns: components