code: def inplace_csr_column_scale(X, scale):
      """Inplace column scaling of a CSR matrix.
  
      Scale each feature of the data matrix by multiplying with specific scale
      provided by the caller assuming a (n_samples, n_features) shape.
  
      Parameters
      ----------
      X : sparse matrix of shape (n_samples, n_features)
          Matrix to normalize using the variance of the features.
          It should be of CSR format.
  
      scale : ndarray of shape (n_features,), dtype={np.float32, np.float64}
          Array of precomputed feature-wise values to use for scaling.
  
      Examples
      --------
      >>> from sklearn.utils import sparsefuncs
      >>> from scipy import sparse
      >>> import numpy as np
      >>> indptr = np.array([0, 3, 4, 4, 4])
      >>> indices = np.array([0, 1, 2, 2])
      >>> data = np.array([8, 1, 2, 5])
      >>> scale = np.array([2, 3, 2])
      >>> csr = sparse.csr_matrix((data, indices, indptr))
      >>> csr.todense()
      matrix([[8, 1, 2],
              [0, 0, 5],
              [0, 0, 0],
              [0, 0, 0]])
      >>> sparsefuncs.inplace_csr_column_scale(csr, scale)
      >>> csr.todense()
      matrix([[16,  3,  4],
              [ 0,  0, 10],
              [ 0,  0,  0],
              [ 0,  0,  0]])
      """
      assert scale.shape[0] == X.shape[1]
      X.data *= scale.take(X.indices, mode="clip")
  
imports: import numpy
  import scipy.sparse
  from scipy.sparse.linalg import LinearOperator
  from ..utils.fixes import _sparse_min_max, _sparse_nan_min_max
  from ..utils.validation import _check_sample_weight
  from .sparsefuncs_fast import csc_mean_variance_axis0
  from .sparsefuncs_fast import csr_mean_variance_axis0
  from .sparsefuncs_fast import incr_mean_variance_axis0
label_desc: Use image node_id 3 for calling a global function with example usage: inplace_csr_column_scale(X, scale) without return types