code: def test_string_elementwise_predicates(
      self, compval, missing, labelarray_dtype
  ):
      if labelarray_dtype == bytes_dtype:
          compval = compval.encode("utf-8")
          missing = missing.encode("utf-8")
  
          startswith_re = b"^" + compval + b".*"
          endswith_re = b".*" + compval + b"$"
          substring_re = b".*" + compval + b".*"
      else:
          startswith_re = "^" + compval + ".*"
          endswith_re = ".*" + compval + "$"
          substring_re = ".*" + compval + ".*"
  
      class C(Classifier):
          dtype = categorical_dtype
          missing_value = missing
          inputs = ()
          window_length = 0
  
      c = C()
  
      # There's no significance to the values here other than that they
      # contain a mix of the comparison value and other values.
      data = LabelArray(
          np.asarray(
              [
                  ["", "a", "ab", "ba"],
                  ["z", "ab", "a", "ab"],
                  ["aa", "ab", "", "ab"],
                  ["aa", "a", "ba", "ba"],
              ],
              dtype=labelarray_dtype,
          ),
          missing_value=missing,
      )
  
      terms = {
          "startswith": c.startswith(compval),
          "endswith": c.endswith(compval),
          "has_substring": c.has_substring(compval),
          # Equivalent filters using regex matching.
          "startswith_re": c.matches(startswith_re),
          "endswith_re": c.matches(endswith_re),
          "has_substring_re": c.matches(substring_re),
      }
  
      expected = {
          "startswith": (data.startswith(compval) & (data != missing)),
          "endswith": (data.endswith(compval) & (data != missing)),
          "has_substring": (
              data.has_substring(compval) & (data != missing)
          ),
      }
      for key in list(expected):
          expected[key + "_re"] = expected[key]
  
      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 8 for calling the ClassifierTestCase obj's underlying member method code with example usage: obj.test_string_elementwise_predicates(compval, missing, labelarray_dtype) without return types