code: def _get_mask(X, value_to_mask):
      """Compute the boolean mask X == missing_values."""
      if value_to_mask == "NaN" or np.isnan(value_to_mask):
          return np.isnan(X)
      else:
          return X == value_to_mask
  
imports: import warnings
  import numpy
  import numpy.ma
  from scipy import sparse
  from scipy import stats
  from ..base import BaseEstimator, TransformerMixin
  from ..utils import check_array
  from ..utils.fixes import astype
  from ..utils.sparsefuncs import _get_median
  from ..utils.validation import check_is_fitted
  from ..utils.validation import FLOAT_DTYPES
  from ..externals import six
label_desc: Use image node_id 1 for calling a global function with example usage: _get_mask(X, value_to_mask) and returns: np, unknown