tensorflow_model_analysis/post_export_metrics/post_export_metrics.py [730:752]:
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  def check_compatibility(self, features_dict: types.TensorTypeMaybeDict,
                          predictions_dict: types.TensorTypeMaybeDict,
                          labels_dict: types.TensorTypeMaybeDict) -> None:
    _check_feature_present(features_dict, self._example_weight_key)
    self._get_labels_and_predictions(predictions_dict, labels_dict)

  def get_metric_ops(
      self, features_dict: types.TensorTypeMaybeDict,
      predictions_dict: types.TensorTypeMaybeDict,
      labels_dict: types.TensorTypeMaybeDict
  ) -> Dict[str, Tuple[types.TensorType, types.TensorType]]:
    # Note that we have to squeeze predictions, labels, weights so they are all
    # N element vectors (otherwise some of them might be N x 1 tensors, and
    # multiplying a N element vector with a N x 1 tensor uses matrix
    # multiplication rather than element-wise multiplication).
    predictions, labels = self._get_labels_and_predictions(
        predictions_dict, labels_dict)
    predictions = _flatten_to_one_dim(tf.cast(predictions, tf.float64))
    labels = _flatten_to_one_dim(tf.cast(labels, tf.float64))
    weights = tf.ones_like(predictions)
    if self._example_weight_key:
      weights = _flatten_to_one_dim(
          tf.cast(features_dict[self._example_weight_key], tf.float64))
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tensorflow_model_analysis/post_export_metrics/post_export_metrics.py [1418:1440]:
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  def check_compatibility(self, features_dict: types.TensorTypeMaybeDict,
                          predictions_dict: types.TensorTypeMaybeDict,
                          labels_dict: types.TensorTypeMaybeDict) -> None:
    _check_feature_present(features_dict, self._example_weight_key)
    self._get_labels_and_predictions(predictions_dict, labels_dict)

  def get_metric_ops(
      self, features_dict: types.TensorTypeMaybeDict,
      predictions_dict: types.TensorTypeMaybeDict,
      labels_dict: types.TensorTypeMaybeDict
  ) -> Dict[str, Tuple[types.TensorType, types.TensorType]]:
    # Note that we have to squeeze predictions, labels, weights so they are all
    # N element vectors (otherwise some of them might be N x 1 tensors, and
    # multiplying a N element vector with a N x 1 tensor uses matrix
    # multiplication rather than element-wise multiplication).
    predictions, labels = self._get_labels_and_predictions(
        predictions_dict, labels_dict)
    predictions = _flatten_to_one_dim(tf.cast(predictions, tf.float64))
    labels = _flatten_to_one_dim(tf.cast(labels, tf.float64))
    weights = tf.ones_like(predictions)
    if self._example_weight_key:
      weights = _flatten_to_one_dim(
          tf.cast(features_dict[self._example_weight_key], tf.float64))
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