class_docstr: Represents sequences of numeric data.
class_docstr_tok: ["Represents","sequences","of","numeric","data","."]
docstr: Returns a `TensorSequenceLengthPair`.
  
  Args:
    transformation_cache: A `FeatureTransformationCache` object to access
      features.
    state_manager: A `StateManager` to create / access resources such as
      lookup tables.
docstr_tok: ["Returns","a","`","TensorSequenceLengthPair","`",".","Args",":","transformation_cache",":","A","`","FeatureTransformationCache","`","object","to","access","features",".","state_manager",":","A","`","StateManager","`","to","create","\/","access","resources","such","as","lookup","tables","."]
code: def get_sequence_dense_tensor(
      self, transformation_cache, state_manager
  ):
      """Returns a `TensorSequenceLengthPair`.
  
      Args:
        transformation_cache: A `FeatureTransformationCache` object to access
          features.
        state_manager: A `StateManager` to create / access resources such as
          lookup tables.
      """
      sp_tensor = transformation_cache.get(self, state_manager)
      dense_tensor = sparse_ops.sparse_tensor_to_dense(
          sp_tensor, default_value=self.default_value
      )
      # Reshape into [batch_size, T, variable_shape].
      dense_shape = array_ops.concat(
          [
              array_ops.shape(dense_tensor)[:1],
              [-1],
              self.variable_shape,
          ],
          axis=0,
      )
      dense_tensor = array_ops.reshape(dense_tensor, shape=dense_shape)
  
      # Get the number of timesteps per example
      # For the 2D case, the raw values are grouped according to num_elements;
      # for the 3D case, the grouping happens in the third dimension, and
      # sequence length is not affected.
      if sp_tensor.shape.ndims == 2:
          num_elements = self.variable_shape.num_elements()
      else:
          num_elements = 1
      seq_length = fc_utils.sequence_length_from_sparse_tensor(
          sp_tensor, num_elements=num_elements
      )
  
      return fc.SequenceDenseColumn.TensorSequenceLengthPair(
          dense_tensor=dense_tensor, sequence_length=seq_length
      )
  
imports: import collections
  from tensorflow.python.feature_column import feature_column_v2
  from tensorflow.python.feature_column import serialization
  from tensorflow.python.feature_column import utils
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import parsing_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
  from tensorflow.tools.docs import doc_controls
label_desc: Use image node_id 6 for calling the SequenceNumericColumn obj's underlying member method code with example usage: obj.get_sequence_dense_tensor(transformation_cache, state_manager) and returns: fc