code: def make_low_rank_matrix(
      n_samples=100,
      n_features=100,
      effective_rank=10,
      tail_strength=0.5,
      random_state=None,
  ):
      """Generate a mostly low rank matrix with bell-shaped singular values
  
      Most of the variance can be explained by a bell-shaped curve of width
      effective_rank: the low rank part of the singular values profile is::
  
          (1 - tail_strength) * exp(-1.0 * (i / effective_rank) ** 2)
  
      The remaining singular values' tail is fat, decreasing as::
  
          tail_strength * exp(-0.1 * i / effective_rank).
  
      The low rank part of the profile can be considered the structured
      signal part of the data while the tail can be considered the noisy
      part of the data that cannot be summarized by a low number of linear
      components (singular vectors).
  
      This kind of singular profiles is often seen in practice, for instance:
       - gray level pictures of faces
       - TF-IDF vectors of text documents crawled from the web
  
      Read more in the :ref:`User Guide <sample_generators>`.
  
      Parameters
      ----------
      n_samples : int, optional (default=100)
          The number of samples.
  
      n_features : int, optional (default=100)
          The number of features.
  
      effective_rank : int, optional (default=10)
          The approximate number of singular vectors required to explain most of
          the data by linear combinations.
  
      tail_strength : float between 0.0 and 1.0, optional (default=0.5)
          The relative importance of the fat noisy tail of the singular values
          profile.
  
      random_state : int, RandomState instance or None, optional (default=None)
          If int, random_state is the seed used by the random number generator;
          If RandomState instance, random_state is the random number generator;
          If None, the random number generator is the RandomState instance used
          by `np.random`.
  
      Returns
      -------
      X : array of shape [n_samples, n_features]
          The matrix.
      """
      generator = check_random_state(random_state)
      n = min(n_samples, n_features)
  
      # Random (ortho normal) vectors
      u, _ = linalg.qr(generator.randn(n_samples, n), mode="economic")
      v, _ = linalg.qr(generator.randn(n_features, n), mode="economic")
  
      # Index of the singular values
      singular_ind = np.arange(n, dtype=np.float64)
  
      # Build the singular profile by assembling signal and noise components
      low_rank = (1 - tail_strength) * np.exp(
          -1.0 * (singular_ind / effective_rank) ** 2
      )
      tail = tail_strength * np.exp(
          -0.1 * singular_ind / effective_rank
      )
      s = np.identity(n) * (low_rank + tail)
  
      return np.dot(np.dot(u, s), v.T)
  
imports: import numbers
  import array
  import numpy
  from scipy import linalg
  import scipy.sparse
  from ..preprocessing import MultiLabelBinarizer
  from ..utils import check_array, check_random_state
  from ..utils import shuffle
  from ..utils.fixes import astype
  from ..utils.random import sample_without_replacement
  from ..externals import six
label_desc: Use image node_id 12 for calling a global function with example usage: make_low_rank_matrix(n_samples, n_features, effective_rank, tail_strength, random_state) and returns: np