class_docstr: A Pipeline expression computing a categorical output.
  
  Classifiers are most commonly useful for describing grouping keys for
  complex transformations on Factor outputs. For example, Factor.demean() and
  Factor.zscore() can be passed a Classifier in their ``groupby`` argument,
  indicating that means/standard deviations should be computed on assets for
  which the classifier produced the same label.
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code: def _principal_computable_term_type(cls):
      return Classifier
  
imports: from functools import partial
  from numbers import Number
  import operator
  import re
  from numpy import where, isnan, nan, zeros
  import pandas
  from zipline.errors import UnsupportedDataType
  from zipline.lib.labelarray import LabelArray
  from zipline.lib.quantiles import quantiles
  from zipline.pipeline.api_utils import restrict_to_dtype
  from zipline.pipeline.dtypes import CLASSIFIER_DTYPES, FACTOR_DTYPES, FILTER_DTYPES
  from zipline.pipeline.sentinels import NotSpecified
  from zipline.pipeline.term import ComputableTerm
  from zipline.utils.compat import unicode
  from zipline.utils.input_validation import expect_types, expect_dtypes
  from zipline.utils.numpy_utils import categorical_dtype, int64_dtype, vectorized_is_element
  from ..filters import ArrayPredicate, NumExprFilter
  from ..mixins import CustomTermMixin, LatestMixin, PositiveWindowLengthMixin, RestrictedDTypeMixin, SingleInputMixin, StandardOutputs
label_desc: Use image node_id 12 for calling the Classifier obj's underlying member method code with example usage: obj._principal_computable_term_type(cls) and returns: Classifier