easy_rec/python/compat/feature_column/feature_column_v2.py [2806:2855]:
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  def transform_feature(self, transformation_cache, state_manager):
    """Returns bucketized categorical `source_column` tensor."""
    source_tensor = transformation_cache.get(self.source_column, state_manager)
    return math_ops._bucketize(  # pylint: disable=protected-access
        source_tensor,
        boundaries=self.boundaries)

  @property
  def variable_shape(self):
    """See `DenseColumn` base class."""
    return tensor_shape.TensorShape(
        tuple(self.source_column.shape) + (len(self.boundaries) + 1,))

  @property
  @deprecation.deprecated(_FEATURE_COLUMN_DEPRECATION_DATE,
                          _FEATURE_COLUMN_DEPRECATION)
  def _variable_shape(self):
    return self.variable_shape

  def _get_dense_tensor_for_input_tensor(self, input_tensor):
    return array_ops.one_hot(
        indices=math_ops.cast(input_tensor, dtypes.int64),
        depth=len(self.boundaries) + 1,
        on_value=1.,
        off_value=0.)

  def get_dense_tensor(self, transformation_cache, state_manager):
    """Returns one hot encoded dense `Tensor`."""
    input_tensor = transformation_cache.get(self, state_manager)
    return self._get_dense_tensor_for_input_tensor(input_tensor)

  @deprecation.deprecated(_FEATURE_COLUMN_DEPRECATION_DATE,
                          _FEATURE_COLUMN_DEPRECATION)
  def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None):
    del weight_collections
    del trainable
    input_tensor = inputs.get(self)
    return self._get_dense_tensor_for_input_tensor(input_tensor)

  @property
  def num_buckets(self):
    """See `CategoricalColumn` base class."""
    # By construction, source_column is always one-dimensional.
    return (len(self.boundaries) + 1) * self.source_column.shape[0]

  @property
  @deprecation.deprecated(_FEATURE_COLUMN_DEPRECATION_DATE,
                          _FEATURE_COLUMN_DEPRECATION)
  def _num_buckets(self):
    return self.num_buckets
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easy_rec/python/compat/feature_column/feature_column_v2.py [2967:3016]:
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  def transform_feature(self, transformation_cache, state_manager):
    """Returns bucketized categorical `source_column` tensor."""
    source_tensor = transformation_cache.get(self.source_column, state_manager)
    return math_ops._bucketize(  # pylint: disable=protected-access
        source_tensor,
        boundaries=self.boundaries)

  @property
  def variable_shape(self):
    """See `DenseColumn` base class."""
    return tensor_shape.TensorShape(
        tuple(self.source_column.shape) + (len(self.boundaries) + 1,))

  @property
  @deprecation.deprecated(_FEATURE_COLUMN_DEPRECATION_DATE,
                          _FEATURE_COLUMN_DEPRECATION)
  def _variable_shape(self):
    return self.variable_shape

  def _get_dense_tensor_for_input_tensor(self, input_tensor):
    return array_ops.one_hot(
        indices=math_ops.cast(input_tensor, dtypes.int64),
        depth=len(self.boundaries) + 1,
        on_value=1.,
        off_value=0.)

  def get_dense_tensor(self, transformation_cache, state_manager):
    """Returns one hot encoded dense `Tensor`."""
    input_tensor = transformation_cache.get(self, state_manager)
    return self._get_dense_tensor_for_input_tensor(input_tensor)

  @deprecation.deprecated(_FEATURE_COLUMN_DEPRECATION_DATE,
                          _FEATURE_COLUMN_DEPRECATION)
  def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None):
    del weight_collections
    del trainable
    input_tensor = inputs.get(self)
    return self._get_dense_tensor_for_input_tensor(input_tensor)

  @property
  def num_buckets(self):
    """See `CategoricalColumn` base class."""
    # By construction, source_column is always one-dimensional.
    return (len(self.boundaries) + 1) * self.source_column.shape[0]

  @property
  @deprecation.deprecated(_FEATURE_COLUMN_DEPRECATION_DATE,
                          _FEATURE_COLUMN_DEPRECATION)
  def _num_buckets(self):
    return self.num_buckets
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