code: def random(
      n_samples, n_components=2, random_state=None, verbose=False
  ):
      """Initialize an embedding using samples from an isotropic Gaussian.
  
      Parameters
      ----------
      n_samples: Union[int, np.ndarray]
          The number of samples. Also accepts a data matrix.
  
      n_components: int
          The dimension of the embedding space.
  
      random_state: Union[int, RandomState]
          If the value is an int, random_state is the seed used by the random
          number generator. If the value is a RandomState instance, then it will
          be used as the random number generator. If the value is None, the random
          number generator is the RandomState instance used by `np.random`.
  
      verbose: bool
  
      Returns
      -------
      initialization: np.ndarray
  
      """
      random_state = check_random_state(random_state)
      if isinstance(n_samples, np.ndarray):
          n_samples = n_samples.shape[0]
      embedding = random_state.normal(
          0, 1e-4, (n_samples, n_components)
      )
      return np.ascontiguousarray(embedding)
  
file_path: openTSNE/openTSNE/initialization.py
imports: import numpy
  import scipy.sparse
  from sklearn.decomposition import PCA
  from sklearn.utils import check_random_state
  from openTSNE import utils
label_desc: Use image node_id 3 for calling a global function with example usage: random(n_samples, n_components, random_state, verbose) and returns: np