docstr: Element of is well-defined for integral classifiers.
code: def test_element_of_integral(self):
      """
      Element of is well-defined for integral classifiers.
      """
  
      class C(Classifier):
          dtype = int64_dtype
          missing_value = -1
          inputs = ()
          window_length = 0
  
      c = C()
  
      # There's no significance to the values here other than that they
      # contain a mix of missing and non-missing values.
      data = np.array(
          [[-1, 1, 0, 2], [3, 0, 1, 0], [-5, 0, -1, 0], [-3, 1, 2, 2]],
          dtype=int64_dtype,
      )
  
      terms = {}
      expected = {}
      for choices in [(0,), (0, 1), (0, 1, 2)]:
          terms[str(choices)] = c.element_of(choices)
          expected[str(choices)] = reduce(
              op.or_,
              (data == elem for elem in choices),
              np.zeros_like(data, dtype=bool),
          )
  
      self.check_terms(
          terms=terms,
          expected=expected,
          initial_workspace={c: data},
          mask=self.build_mask(self.ones_mask(shape=data.shape)),
      )
  
imports: from functools import reduce
  import operator
  import numpy
  import pandas
  from zipline.lib.labelarray import LabelArray
  from zipline.pipeline import Classifier
  from zipline.pipeline.data.testing import TestingDataSet
  from zipline.pipeline.expression import methods_to_ops
  from zipline.testing import parameter_space
  from zipline.testing.fixtures import ZiplineTestCase
  from zipline.testing.predicates import assert_equal
  from zipline.utils.numpy_utils import categorical_dtype, int64_dtype
  from .base import BaseUSEquityPipelineTestCase
label_desc: Use image node_id 10 for calling the ClassifierTestCase obj's underlying member method code with example usage: obj.test_element_of_integral() without return types