code: def inplace_swap_column(X, m, n):
      """
      Swap two columns of a CSC/CSR matrix in-place.
  
      Parameters
      ----------
      X : sparse matrix of shape (n_samples, n_features)
          Matrix whose two columns are to be swapped. It should be of
          CSR or CSC format.
  
      m : int
          Index of the column of X to be swapped.
  
      n : int
          Index of the column of X to be swapped.
  
      Examples
      --------
      >>> from sklearn.utils import sparsefuncs
      >>> from scipy import sparse
      >>> import numpy as np
      >>> indptr = np.array([0, 2, 3, 3, 3])
      >>> indices = np.array([0, 2, 2])
      >>> data = np.array([8, 2, 5])
      >>> csr = sparse.csr_matrix((data, indices, indptr))
      >>> csr.todense()
      matrix([[8, 0, 2],
              [0, 0, 5],
              [0, 0, 0],
              [0, 0, 0]])
      >>> sparsefuncs.inplace_swap_column(csr, 0, 1)
      >>> csr.todense()
      matrix([[0, 8, 2],
              [0, 0, 5],
              [0, 0, 0],
              [0, 0, 0]])
      """
      if m < 0:
          m += X.shape[1]
      if n < 0:
          n += X.shape[1]
      if sp.issparse(X) and X.format == "csc":
          inplace_swap_row_csr(X, m, n)
      elif sp.issparse(X) and X.format == "csr":
          inplace_swap_row_csc(X, m, n)
      else:
          _raise_typeerror(X)
  
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 12 for calling a global function with example usage: inplace_swap_column(X, m, n) without return types