class_docstr: A RelativeMultiHeadAttention layer as described in [3].
  
  Uses segment level recurrence with state reuse.
class_docstr_tok: ["A","RelativeMultiHeadAttention","layer","as","described","in","[","3","]",".","Uses","segment","level","recurrence","with","state","reuse","."]
code: def rel_shift(x: TensorType) -> TensorType:
      # Transposed version of the shift approach described in [3].
      # https://github.com/kimiyoung/transformer-xl/blob/
      # 44781ed21dbaec88b280f74d9ae2877f52b492a5/tf/model.py#L31
      x_size = list(x.shape)
  
      x = torch.nn.functional.pad(x, (0, 0, 1, 0, 0, 0, 0, 0))
      x = torch.reshape(
          x, [x_size[0], x_size[2] + 1, x_size[1], x_size[3]]
      )
      x = x[:, 1:, :, :]
      x = torch.reshape(x, x_size)
  
      return x
  
file_path: ray/rllib/models/torch/modules/relative_multi_head_attention.py
imports: from typing import Union
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.models.torch.misc import SlimFC
  from ray.rllib.utils.torch_utils import sequence_mask
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 3 for calling the RelativeMultiHeadAttention obj's underlying member method code with example usage: obj.rel_shift(x) and returns: x