class_docstr: Base class for user-defined Classifiers.
  
  Does not suppport multiple outputs.
  
  See Also
  --------
  catalyst.pipeline.CustomFactor
  catalyst.pipeline.CustomFilter
class_docstr_tok: ["Base","class","for","user-defined","Classifiers",".","Does","not","suppport","multiple","outputs",".","See","Also","--","--","--","--","catalyst.pipeline.CustomFactor","catalyst.pipeline.CustomFilter"]
docstr: Override the default array allocation to produce a LabelArray when we
  have a string-like dtype.
docstr_tok: ["Override","the","default","array","allocation","to","produce","a","LabelArray","when","we","have","a","string-like","dtype","."]
code: def _allocate_output(self, windows, shape):
      """
      Override the default array allocation to produce a LabelArray when we
      have a string-like dtype.
      """
      if self.dtype == int64_dtype:
          return super(CustomClassifier, self)._allocate_output(
              windows,
              shape,
          )
  
      # This is a little bit of a hack.  We might not know what the
      # categories for a LabelArray are until it's actually been loaded, so
      # we need to look at the underlying data.
      return windows[0].data.empty_like(shape)
  
imports: from numbers import Number
  import operator
  import re
  from numpy import where, isnan, nan, zeros
  import pandas
  from catalyst.lib.labelarray import LabelArray
  from catalyst.lib.quantiles import quantiles
  from catalyst.pipeline.api_utils import restrict_to_dtype
  from catalyst.pipeline.sentinels import NotSpecified
  from catalyst.pipeline.term import ComputableTerm
  from catalyst.utils.compat import unicode
  from catalyst.utils.input_validation import expect_types, expect_dtypes
  from catalyst.utils.memoize import classlazyval
  from catalyst.utils.numpy_utils import categorical_dtype, int64_dtype, vectorized_is_element
  from ..filters import ArrayPredicate, NotNullFilter, NullFilter, NumExprFilter
  from ..mixins import AliasedMixin, CustomTermMixin, DownsampledMixin, LatestMixin, PositiveWindowLengthMixin, RestrictedDTypeMixin, SingleInputMixin, StandardOutputs
label_desc: Use image node_id 1 for calling the CustomClassifier obj's underlying member method code with example usage: obj._allocate_output(windows, shape) and returns: windows, super