code: def _implicit_column_offset(X, offset):
      """Create an implicitly offset linear operator.
  
      This is used by PCA on sparse data to avoid densifying the whole data
      matrix.
  
      Params
      ------
          X : sparse matrix of shape (n_samples, n_features)
          offset : ndarray of shape (n_features,)
  
      Returns
      -------
      centered : LinearOperator
      """
      offset = offset[None, :]
      XT = X.T
      return LinearOperator(
          matvec=lambda x: X @ x - offset @ x,
          matmat=lambda x: X @ x - offset @ x,
          rmatvec=lambda x: XT @ x - (offset * x.sum()),
          rmatmat=lambda x: XT @ x - offset.T @ x.sum(axis=0)[None, :],
          dtype=X.dtype,
          shape=X.shape,
      )
  
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 18 for calling a global function with example usage: _implicit_column_offset(X, offset) and returns: LinearOperator