class_docstr: Base class for all imputers.
  
  It adds automatically support for `add_indicator`.
class_docstr_tok: ["Base","class","for","all","imputers",".","It","adds","automatically","support","for","`","add_indicator","`","."]
docstr: Compute the indicator mask.'
  
  Note that X must be the original data as passed to the imputer before
  any imputation, since imputation may be done inplace in some cases.
docstr_tok: ["Compute","the","indicator","mask",".","'","Note","that","X","must","be","the","original","data","as","passed","to","the","imputer","before","any","imputation",",","since","imputation","may","be","done","inplace","in","some","cases","."]
code: def _transform_indicator(self, X):
      """Compute the indicator mask.'
  
      Note that X must be the original data as passed to the imputer before
      any imputation, since imputation may be done inplace in some cases.
      """
      if self.add_indicator:
          if not hasattr(self, "indicator_"):
              raise ValueError(
                  "Make sure to call _fit_indicator before _transform_indicator"
              )
          return self.indicator_.transform(X)
  
imports: import numbers
  import warnings
  from collections import Counter
  from functools import partial
  from typing import Callable
  import numpy
  import numpy.ma
  from scipy import sparse
  from ..base import BaseEstimator, TransformerMixin, _fit_context
  from ..utils import _is_pandas_na, is_scalar_nan
  from ..utils._mask import _get_mask
  from ..utils._param_validation import MissingValues, StrOptions
  from ..utils.fixes import _mode
  from ..utils.sparsefuncs import _get_median
  from ..utils.validation import FLOAT_DTYPES, _check_feature_names_in, check_is_fitted
label_desc: Use image node_id 3 for calling the _BaseImputer obj's underlying member method code with example usage: obj._transform_indicator(X) and returns: self