easy_rec/python/compat/feature_column/feature_column.py [2872:2917]:
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    return embedding_ops.safe_embedding_lookup_sparse(
        embedding_weights=embedding_weights,
        sparse_ids=sparse_ids,
        sparse_weights=sparse_weights,
        combiner=self.combiner,
        name='%s_weights' % self.name,
        max_norm=self.max_norm)

  def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None):
    if isinstance(self.categorical_column, _SequenceCategoricalColumn):
      raise ValueError(
          'In embedding_column: {}. '
          'categorical_column must not be of type _SequenceCategoricalColumn. '
          'Suggested fix A: If you wish to use input_layer, use a '
          'non-sequence categorical_column_with_*. '
          'Suggested fix B: If you wish to create sequence input, use '
          'sequence_input_layer instead of input_layer. '
          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
                                       self.categorical_column))
    return self._get_dense_tensor_internal(
        inputs=inputs,
        weight_collections=weight_collections,
        trainable=trainable)

  def _get_sequence_dense_tensor(self,
                                 inputs,
                                 weight_collections=None,
                                 trainable=None):
    if not isinstance(self.categorical_column, _SequenceCategoricalColumn):
      raise ValueError(
          'In embedding_column: {}. '
          'categorical_column must be of type _SequenceCategoricalColumn '
          'to use sequence_input_layer. '
          'Suggested fix: Use one of sequence_categorical_column_with_*. '
          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
                                       self.categorical_column))
    dense_tensor = self._get_dense_tensor_internal(  # pylint: disable=protected-access
        inputs=inputs,
        weight_collections=weight_collections,
        trainable=trainable)

    sparse_tensors = self.categorical_column._get_sparse_tensors(inputs)  # pylint: disable=protected-access
    sequence_length = fc_utils.sequence_length_from_sparse_tensor(
        sparse_tensors.id_tensor)
    return _SequenceDenseColumn.TensorSequenceLengthPair(
        dense_tensor=dense_tensor, sequence_length=sequence_length)
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easy_rec/python/compat/feature_column/feature_column.py [3062:3106]:
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      return embedding_ops.safe_embedding_lookup_sparse(
          embedding_weights=embedding_weights,
          sparse_ids=sparse_ids,
          sparse_weights=sparse_weights,
          combiner=self.combiner,
          name='%s_weights' % self.name,
          max_norm=self.max_norm)

  def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None):
    if isinstance(self.categorical_column, _SequenceCategoricalColumn):
      raise ValueError(
          'In embedding_column: {}. '
          'categorical_column must not be of type _SequenceCategoricalColumn. '
          'Suggested fix A: If you wish to use input_layer, use a '
          'non-sequence categorical_column_with_*. '
          'Suggested fix B: If you wish to create sequence input, use '
          'sequence_input_layer instead of input_layer. '
          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
                                       self.categorical_column))
    return self._get_dense_tensor_internal(
        inputs=inputs,
        weight_collections=weight_collections,
        trainable=trainable)

  def _get_sequence_dense_tensor(self,
                                 inputs,
                                 weight_collections=None,
                                 trainable=None):
    if not isinstance(self.categorical_column, _SequenceCategoricalColumn):
      raise ValueError(
          'In embedding_column: {}. '
          'categorical_column must be of type _SequenceCategoricalColumn '
          'to use sequence_input_layer. '
          'Suggested fix: Use one of sequence_categorical_column_with_*. '
          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
                                       self.categorical_column))
    dense_tensor = self._get_dense_tensor_internal(  # pylint: disable=protected-access
        inputs=inputs,
        weight_collections=weight_collections,
        trainable=trainable)
    sparse_tensors = self.categorical_column._get_sparse_tensors(inputs)  # pylint: disable=protected-access
    sequence_length = fc_utils.sequence_length_from_sparse_tensor(
        sparse_tensors.id_tensor)
    return _SequenceDenseColumn.TensorSequenceLengthPair(
        dense_tensor=dense_tensor, sequence_length=sequence_length)
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