code: def _masked_column_mean(arr, masked_value):
      """Compute the mean of each column in the 2D array arr, ignoring any
      instances of masked_value"""
      mask = _get_mask(arr, masked_value)
      count_missing_values = mask.sum(axis=0)
      n_elems = arr.shape[0] - count_missing_values
      mean = cp.nansum(arr, axis=0)
      if not cp.isnan(masked_value):
          mean -= count_missing_values * masked_value
      mean /= n_elems
      return mean
  
file_path: cuml/python/cuml/thirdparty_adapters/adapters.py
imports: from cupyx.scipy import sparse
  from scipy import sparse
  from scipy.sparse import csc_matrix
  from scipy.sparse import csc_matrix
  from cuml.internals.safe_imports import cpu_only_import_from
  from cupyx.scipy.sparse import csc_matrix
  from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.internals.global_settings import GlobalSettings
  from cuml.internals.input_utils import input_to_cupy_array, input_to_host_array
  from cuml.internals.safe_imports import gpu_only_import
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 7 for calling a global function with example usage: _masked_column_mean(arr, masked_value) and returns: mean