class_docstr: A table that can store Episodes of variable length.
class_docstr_tok: ["A","table","that","can","store","Episodes","of","variable","length","."]
docstr: Stacks a slot list, restoring dtype and shape information.
  
  Going through the Variable loses all TensorList dtype and
  static element shape info.  Setting dtype, shape, and adding batch
  dimension for the length of the list.
  
  Args:
    slot: The slot corresponding to tensor_list
    tensor_list: TensorList of values stored in a scalar Tensor.
  
  Returns:
    A Tensor with first dimension equal to the length of tensor_list.
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code: def _stack_tensor_list(self, slot, tensor_list):
      """Stacks a slot list, restoring dtype and shape information.
  
      Going through the Variable loses all TensorList dtype and
      static element shape info.  Setting dtype, shape, and adding batch
      dimension for the length of the list.
  
      Args:
        slot: The slot corresponding to tensor_list
        tensor_list: TensorList of values stored in a scalar Tensor.
  
      Returns:
        A Tensor with first dimension equal to the length of tensor_list.
      """
      tensor_list.shape.assert_has_rank(0)
      value = list_ops.tensor_list_stack(
          tensor_list, self._slot2spec_map[slot].dtype
      )
      value.set_shape(
          [None] + self._slot2spec_map[slot].shape.as_list()
      )
      return value
  
file_path: agents/tf_agents/replay_buffers/episodic_table.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_agents.utils import nest_utils
  from tensorflow.python.ops import list_ops
label_desc: Use image node_id 4 for calling the EpisodicTable obj's underlying member method code with example usage: obj._stack_tensor_list(slot, tensor_list) and returns: value