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.
      """
      assert scale.shape[0] == X.shape[1]
      X.data *= scale.take(X.indices, mode="clip")
  
imports: import numpy
  import scipy.sparse
  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