class_docstr: Represents a variable-length list of items from spaces.Repeated.
  
  RepeatedValues are created when you use spaces.Repeated, and are
  accessible as part of input_dict["obs"] in ModelV2 forward functions.
  
  Example:
      Suppose the gym space definition was:
          Repeated(Repeated(Box(K), N), M)
  
      Then in the model forward function, input_dict["obs"] is of type:
          RepeatedValues(RepeatedValues(<Tensor shape=(B, M, N, K)>))
  
      The tensor is accessible via:
          input_dict["obs"].values.values
  
      And the actual data lengths via:
          # outer repetition, shape [B], range [0, M]
          input_dict["obs"].lengths
              -and-
          # inner repetition, shape [B, M], range [0, N]
          input_dict["obs"].values.lengths
  
  Attributes:
      values: The padded data tensor of shape [B, max_len, ..., sz],
          where B is the batch dimension, max_len is the max length of this
          list, followed by any number of sub list max lens, followed by the
          actual data size.
      lengths (List[int]): Tensor of shape [B, ...] that represents the
          number of valid items in each list. When the list is nested within
          other lists, there will be extra dimensions for the parent list
          max lens.
      max_len: The max number of items allowed in each list.
  
  TODO(ekl): support conversion to tf.RaggedTensor.
docstr: Unbatches the repeat dimension (the one `max_len` in size).
  
  This removes the repeat dimension. The result will be a Python list of
  with length `self.max_len`. Note that the data is still padded.
  
  .. testcode::
      :skipif: True
  
      batch = RepeatedValues(<Tensor shape=(B, N, K)>)
      items = batch.unbatch()
      len(items) == batch.max_len
  
  .. testoutput::
  
      True
  
  .. testcode::
      :skipif: True
  
      print(items)
  
  .. testoutput::
  
      [<Tensor_1 shape=(B, K)>, ..., <Tensor_N shape=(B, K)>]
code: def unbatch_repeat_dim(self) -> List[TensorType]:
      """Unbatches the repeat dimension (the one `max_len` in size).
  
      This removes the repeat dimension. The result will be a Python list of
      with length `self.max_len`. Note that the data is still padded.
  
      .. testcode::
          :skipif: True
  
          batch = RepeatedValues(<Tensor shape=(B, N, K)>)
          items = batch.unbatch()
          len(items) == batch.max_len
  
      .. testoutput::
  
          True
  
      .. testcode::
          :skipif: True
  
          print(items)
  
      .. testoutput::
  
          [<Tensor_1 shape=(B, K)>, ..., <Tensor_N shape=(B, K)>]
      """
      return _unbatch_helper(self.values, self.max_len)
  
file_path: ray/rllib/models/repeated_values.py
imports: from typing import List
  from ray.rllib.utils.annotations import PublicAPI
  from ray.rllib.utils.typing import TensorType, TensorStructType
label_desc: Use image node_id 3 for calling the RepeatedValues obj's underlying member method code with example usage: obj.unbatch_repeat_dim() and returns: _unbatch_helper