code: def custom_validate_statistics(
      statistics: statistics_pb2.DatasetFeatureStatisticsList,
      validations: custom_validation_config_pb2.CustomValidationConfig,
      baseline_statistics: Optional[
          statistics_pb2.DatasetFeatureStatisticsList
      ] = None,
      environment: Optional[str] = None,
  ) -> anomalies_pb2.Anomalies:
      """Validates the input statistics with the user-supplied SQL queries.
  
      If the SQL query from a user-supplied validation returns False, TFDV will
      return an anomaly for that validation. In single feature valdiations, the test
      feature will be mapped to `feature` in the SQL query. In two feature
      validations, the test feature will be mapped to `feature_test` in the SQL
      query, and the base feature will be mapped to `feature_base`.
  
      If an optional `environment` is supplied, TFDV will run validations with
      that environment specified and validations with no environment specified.
  
      NOTE: This function is not supported on Windows.
  
      Args:
        statistics: A DatasetFeatureStatisticsList protocol buffer that holds the
          statistics to validate.
        validations: Configuration that specifies the dataset(s) and feature(s) to
          validate and the SQL query to use for the validation. The SQL query must
          return a boolean value.
        baseline_statistics: An optional DatasetFeatureStatisticsList protocol
          buffer that holds the baseline statistics used when validating feature
          pairs.
        environment: If supplied, TFDV will run validations with that
          environment specified and validations with no environment specified. If
          not supplied, TFDV will run all validations.
      Returns:
        An Anomalies protocol buffer.
      """
      serialized_statistics = statistics.SerializeToString()
      serialized_baseline_statistics = (
          baseline_statistics.SerializeToString()
          if baseline_statistics is not None
          else ""
      )
      serialized_validations = validations.SerializeToString()
      environment = "" if environment is None else environment
      serialized_anomalies = (
          pywrap_tensorflow_data_validation.CustomValidateStatistics(
              tf.compat.as_bytes(serialized_statistics),
              tf.compat.as_bytes(serialized_baseline_statistics),
              tf.compat.as_bytes(serialized_validations),
              tf.compat.as_bytes(environment),
          )
      )
      result = anomalies_pb2.Anomalies()
      result.ParseFromString(serialized_anomalies)
      return result
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  import logging
  from typing import Callable, Iterable, List, Optional, Text, Tuple, Set
  import apache_beam
  import pyarrow
  import tensorflow
  from tensorflow_data_validation import constants
  from tensorflow_data_validation import types
  from tensorflow_data_validation.anomalies.proto import custom_validation_config_pb2
  from tensorflow_data_validation.anomalies.proto import validation_config_pb2
  from tensorflow_data_validation.anomalies.proto import validation_metadata_pb2
  from tensorflow_data_validation.api import validation_options
  from tensorflow_data_validation.pywrap.tensorflow_data_validation_extension import validation
  from tensorflow_data_validation.skew import feature_skew_detector
  from tensorflow_data_validation.skew.protos import feature_skew_results_pb2
  from tensorflow_data_validation.statistics import stats_impl
  from tensorflow_data_validation.statistics import stats_options
  from tensorflow_data_validation.utils import anomalies_util
  from tensorflow_data_validation.utils import slicing_util
  from tensorflow_data_validation.utils import stats_util
  from tensorflow_metadata.proto.v0 import anomalies_pb2
  from tensorflow_metadata.proto.v0 import schema_pb2
  from tensorflow_metadata.proto.v0 import statistics_pb2
label_desc: Use image node_id 8 for calling a global function with example usage: custom_validate_statistics(statistics, validations, baseline_statistics, environment) and returns: result