code: def _reorder_cache(past, beam_idx):
      reordered_past = ()
      for layer_past in past:
          reordered_past += (
              tuple(
                  past_state.index_select(0, beam_idx)
                  for past_state in layer_past
              ),
          )
      return reordered_past
  
imports: import random
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from transformers.models.opt.configuration_opt import OPTConfig
label_desc: Use image node_id 10 for calling the OPTForCausalLM obj's underlying member method code with example usage: obj._reorder_cache(past, beam_idx) and returns: reordered_past