code: def setUp(self):
      super(NaturalLanguageStatsGeneratorTest, self).setUp()
      self._schema = text_format.Parse(
          """
          feature {
            name: "string_nlp_feature_with_vocab"
            type: BYTES
            natural_language_domain {
              vocabulary: "my_vocab"
              coverage {
                excluded_int_tokens: [1]
              }
            }
          }
          feature {
            name: "string_nlp_feature_no_vocab"
            type: BYTES
            natural_language_domain {
              coverage {
                oov_string_tokens: ['Bar', 'Baz']
              }
            }
          }
          feature {
            name: "int_nlp_feature_with_vocab"
            type: INT
            natural_language_domain {
              vocabulary: "my_vocab"
              coverage {
                excluded_string_tokens: ['Foo', 'Caz']
                excluded_int_tokens: [1]
                oov_string_tokens: ['Bar', 'Baz']
              }
              sequence_length_constraints {
                excluded_string_value: ['Razzz']
              }
            }
          }
          feature {
            name: "int_nlp_feature_with_vocab_and_token_constraints"
            type: INT
            natural_language_domain {
              vocabulary: "my_vocab"
              coverage {
                excluded_int_tokens: [1]
              }
              token_constraints {
                string_value: 'Foo'
              }
              token_constraints {
                int_value: 1
              }
            }
          }
          feature {
            name: "int_nlp_feature_no_vocab"
            type: INT
            natural_language_domain {
              coverage {
                excluded_int_tokens: [1]
              }
            }
          }
          feature {
            name: "int_nlp_feature_empty_domain"
            type: INT
            natural_language_domain {}
          }
          feature {
            name: "non_nlp_feature"
            type: BYTES
          }
          """,
          schema_pb2.Schema(),
      )
      self._string_nlp_feature_with_vocab_path = types.FeaturePath(
          ["string_nlp_feature_with_vocab"]
      )
      self._string_nlp_feature_no_vocab_path = types.FeaturePath(
          ["string_nlp_feature_no_vocab"]
      )
      self._int_nlp_feature_with_vocab_path = types.FeaturePath(
          ["int_nlp_feature_with_vocab"]
      )
      self._int_nlp_feature_with_vocab_and_token_constraints_path = (
          types.FeaturePath(
              ["int_nlp_feature_with_vocab_and_token_constraints"]
          )
      )
      self._int_nlp_feature_no_vocab_path = types.FeaturePath(
          ["int_nlp_feature_no_vocab"]
      )
      self._int_nlp_feature_empty_domain = types.FeaturePath(
          ["int_nlp_feature_empty_domain"]
      )
      self._non_nlp_feature_path = types.FeaturePath(
          ["non_nlp_feature"]
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tempfile
  from absl.testing import absltest
  import pyarrow
  from tensorflow_data_validation import types
  from tensorflow_data_validation.statistics.generators import natural_language_stats_generator
  from tensorflow_data_validation.utils import test_util
  from google.protobuf import text_format
  from tensorflow_metadata.proto.v0 import schema_pb2
  from tensorflow_metadata.proto.v0 import statistics_pb2
label_desc: Use image node_id 1 for calling the NaturalLanguageStatsGeneratorTest obj's underlying member method code with example usage: obj.setUp() without return types