class_docstr: DoFn that computes skew for each pair of examples.
class_docstr_tok: ["DoFn","that","computes","skew","for","each","pair","of","examples","."]
docstr: Initializes _ComputeSkew.
  
  Args:
    features_to_ignore: Names of features that are ignored in skew detection.
    float_round_ndigits: Number of digits 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.
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code: def __init__(
      self,
      features_to_ignore: List[tf.train.Feature],
      float_round_ndigits: Optional[int],
      allow_duplicate_identifiers,
      confusion_configs: List[ConfusionConfig],
  ) -> None:
      """Initializes _ComputeSkew.
  
      Args:
        features_to_ignore: Names of features that are ignored in skew detection.
        float_round_ndigits: Number of digits 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.
      """
      self._features_to_ignore = features_to_ignore
      self._float_round_ndigits = float_round_ndigits
      self._allow_duplicate_identifiers = allow_duplicate_identifiers
      self._skipped_duplicate_identifiers_counter = (
          beam.metrics.Metrics.counter(
              constants.METRICS_NAMESPACE, "examplediff_skip_dupe_id"
          )
      )
      self._ids_counter = beam.metrics.Metrics.counter(
          constants.METRICS_NAMESPACE, "examplediff_ids_counter"
      )
      self._pairs_counter = beam.metrics.Metrics.counter(
          constants.METRICS_NAMESPACE, "examplediff_pairs_counter"
      )
      self._confusion_configs = 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 _ComputeSkew object from inherited base classes: beam with example: obj = _ComputeSkew(features_to_ignore, float_round_ndigits, allow_duplicate_identifiers, confusion_configs)