code: def inplace_column_scale(X, scale):
      """Inplace column scaling of a CSC/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 CSC or CSR format.
  
      scale : ndarray of shape (n_features,), dtype={np.float32, np.float64}
          Array of precomputed feature-wise values to use for scaling.
      """
      if sp.issparse(X) and X.format == "csc":
          inplace_csr_row_scale(X.T, scale)
      elif sp.issparse(X) and X.format == "csr":
          inplace_csr_column_scale(X, scale)
      else:
          _raise_typeerror(X)
  
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 7 for calling a global function with example usage: inplace_column_scale(X, scale) without return types