code: def get_attn_bias_and_cat(x_list, branges=None):
      """
      this will perform the index select, cat the tensors, and provide the attn_bias from cache
      """
      batch_sizes = (
          [b.shape[0] for b in branges]
          if branges is not None
          else [x.shape[0] for x in x_list]
      )
      all_shapes = tuple(
          (b, x.shape[1]) for b, x in zip(batch_sizes, x_list)
      )
      if all_shapes not in attn_bias_cache.keys():
          seqlens = []
          for b, x in zip(batch_sizes, x_list):
              for _ in range(b):
                  seqlens.append(x.shape[1])
          attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens)
          attn_bias._batch_sizes = batch_sizes
          attn_bias_cache[all_shapes] = attn_bias
  
      if branges is not None:
          cat_tensors = index_select_cat(
              [x.flatten(1) for x in x_list], branges
          ).view(1, -1, x_list[0].shape[-1])
      else:
          tensors_bs1 = tuple(
              x.reshape([1, -1, *x.shape[2:]]) for x in x_list
          )
          cat_tensors = torch.cat(tensors_bs1, dim=1)
  
      return attn_bias_cache[all_shapes], cat_tensors
  
imports: import logging
  import os
  from typing import Callable, List, Any, Tuple, Dict
  import warnings
  import torch
  from torch import nn, Tensor
  from .attention import Attention, MemEffAttention
  from .drop_path import DropPath
  from .layer_scale import LayerScale
  from .mlp import Mlp
label_desc: Use image node_id 4 for calling a global function with example usage: get_attn_bias_and_cat(x_list, branges) and returns: attn_bias_cache,  cat_tensors