code: def _random_choice_csc(
      n_samples, classes, class_probability=None, random_state=None
  ):
      """Generate a sparse random matrix given column class distributions
  
      Parameters
      ----------
      n_samples : int,
          Number of samples to draw in each column.
  
      classes : list of size n_outputs of arrays of size (n_classes,)
          List of classes for each column.
  
      class_probability : list of size n_outputs of arrays of \
          shape (n_classes,), default=None
          Class distribution of each column. If None, uniform distribution is
          assumed.
  
      random_state : int, RandomState instance or None, default=None
          Controls the randomness of the sampled classes.
          See :term:`Glossary <random_state>`.
  
      Returns
      -------
      random_matrix : sparse csc matrix of size (n_samples, n_outputs)
  
      """
      data = array.array("i")
      indices = array.array("i")
      indptr = array.array("i", [0])
  
      for j in range(len(classes)):
          classes[j] = np.asarray(classes[j])
          if classes[j].dtype.kind != "i":
              raise ValueError(
                  "class dtype %s is not supported" % classes[j].dtype
              )
          classes[j] = classes[j].astype(np.int64, copy=False)
  
          # use uniform distribution if no class_probability is given
          if class_probability is None:
              class_prob_j = np.empty(shape=classes[j].shape[0])
              class_prob_j.fill(1 / classes[j].shape[0])
          else:
              class_prob_j = np.asarray(class_probability[j])
  
          if not np.isclose(np.sum(class_prob_j), 1.0):
              raise ValueError(
                  "Probability array at index {0} does not sum to one".format(
                      j
                  )
              )
  
          if class_prob_j.shape[0] != classes[j].shape[0]:
              raise ValueError(
                  "classes[{0}] (length {1}) and "
                  "class_probability[{0}] (length {2}) have "
                  "different length.".format(
                      j, classes[j].shape[0], class_prob_j.shape[0]
                  )
              )
  
          # If 0 is not present in the classes insert it with a probability 0.0
          if 0 not in classes[j]:
              classes[j] = np.insert(classes[j], 0, 0)
              class_prob_j = np.insert(class_prob_j, 0, 0.0)
  
          # If there are nonzero classes choose randomly using class_probability
          rng = check_random_state(random_state)
          if classes[j].shape[0] > 1:
              index_class_0 = np.flatnonzero(classes[j] == 0).item()
              p_nonzero = 1 - class_prob_j[index_class_0]
              nnz = int(n_samples * p_nonzero)
              ind_sample = sample_without_replacement(
                  n_population=n_samples,
                  n_samples=nnz,
                  random_state=random_state,
              )
              indices.extend(ind_sample)
  
              # Normalize probabilities for the nonzero elements
              classes_j_nonzero = classes[j] != 0
              class_probability_nz = class_prob_j[classes_j_nonzero]
              class_probability_nz_norm = class_probability_nz / np.sum(
                  class_probability_nz
              )
              classes_ind = np.searchsorted(
                  class_probability_nz_norm.cumsum(),
                  rng.uniform(size=nnz),
              )
              data.extend(classes[j][classes_j_nonzero][classes_ind])
          indptr.append(len(indices))
  
      return sp.csc_matrix(
          (data, indices, indptr), (n_samples, len(classes)), dtype=int
      )
  
imports: import array
  import numpy
  import scipy.sparse
  from .None import check_random_state
  from ._random import sample_without_replacement
label_desc: Use image node_id 1 for calling a global function with example usage: _random_choice_csc(n_samples, classes, class_probability, random_state) and returns: sp