class_docstr: Identifies feature skew in baseline and test examples.
  
  This PTransform returns a dict of PCollections containing:
    SKEW_RESULTS_KEY: Aggregated skew statistics (containing, e.g., mismatch
      count, baseline only, test only) for each feature; and
    SKEW_PAIRS_KEY: A sample of skewed example pairs (if sample_size is > 0).
    MATCH_STATS: A PCollection containing a single MatchStats proto.
    CONFUSION_KEY: (if configured) counts of paired feature values.
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docstr: Initializes DetectFeatureSkewImpl.
  
  Args:
    identifier_features: The names of the features to use to identify an
      example.
    features_to_ignore: The names of the features for which skew detection is
      not done.
    sample_size: Size of the sample of baseline-test example pairs that
      exhibit skew to include in the skew results.
    float_round_ndigits: Number of digits of precision after the decimal point
      to which to round float values before detecting skew.
    allow_duplicate_identifiers: If set, skew detection will be done on
      examples for which there are duplicate identifier feature values. In
      this case, the counts in the FeatureSkew result are based on each
      baseline-test example pair analyzed. Examples with given identifier
      feature values must all fit in memory.
    confusion_configs: Optional list of ConfusionConfig objects describing
      per-feature config for value confusion analysis. If provided, the result
      will contain a value keyed under CONFUSION_KEY containing a PCollection
      of ConfusionCount protos.
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code: def __init__(
      self,
      identifier_features: List[types.FeatureName],
      features_to_ignore: Optional[List[types.FeatureName]] = None,
      sample_size: int = 0,
      float_round_ndigits: Optional[int] = None,
      allow_duplicate_identifiers: bool = False,
      confusion_configs: Optional[List[ConfusionConfig]] = None,
  ) -> None:
      """Initializes DetectFeatureSkewImpl.
  
      Args:
        identifier_features: The names of the features to use to identify an
          example.
        features_to_ignore: The names of the features for which skew detection is
          not done.
        sample_size: Size of the sample of baseline-test example pairs that
          exhibit skew to include in the skew results.
        float_round_ndigits: Number of digits of precision after the decimal point
          to which to round float values before detecting skew.
        allow_duplicate_identifiers: If set, skew detection will be done on
          examples for which there are duplicate identifier feature values. In
          this case, the counts in the FeatureSkew result are based on each
          baseline-test example pair analyzed. Examples with given identifier
          feature values must all fit in memory.
        confusion_configs: Optional list of ConfusionConfig objects describing
          per-feature config for value confusion analysis. If provided, the result
          will contain a value keyed under CONFUSION_KEY containing a PCollection
          of ConfusionCount protos.
      """
      if not identifier_features:
          raise ValueError(
              "At least one feature name must be specified in "
              "identifier_features."
          )
      self._identifier_features = identifier_features
      self._sample_size = sample_size
      self._float_round_ndigits = float_round_ndigits
      if features_to_ignore is not None:
          self._features_to_ignore = (
              features_to_ignore + identifier_features
          )
      else:
          self._features_to_ignore = identifier_features
      self._allow_duplicate_identifiers = allow_duplicate_identifiers
      self._confusion_configs = (
          [] if confusion_configs is None else confusion_configs
      )
  
imports: from typing import Any, Dict, Iterable, Iterator, List, Optional, Tuple, Union
  import apache_beam
  import farmhash
  import numpy
  import tensorflow
  from tensorflow_data_validation import constants
  from tensorflow_data_validation import types
  from tensorflow_data_validation.utils import artifacts_io_impl
  from tensorflow_data_validation.skew.protos import feature_skew_results_pb2
label_desc: Use image node_id 1 to create a new DetectFeatureSkewImpl object from inherited base classes: beam with example: obj = DetectFeatureSkewImpl(identifier_features, features_to_ignore, sample_size, float_round_ndigits, allow_duplicate_identifiers, confusion_configs)