code: def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
      ndim = x.ndim
      assert 0 <= 1 < ndim
      assert freqs_cis.shape == (x.shape[1], x.shape[-1])
      shape = [
          d if i == 1 or i == ndim - 1 else 1
          for i, d in enumerate(x.shape)
      ]
      return freqs_cis.view(*shape)
  
imports: import math
  from dataclasses import dataclass
  from typing import Any, Optional, Tuple
  import fairscale.nn.model_parallel.initialize
  import torch
  import torch.nn.functional
  from fairscale.nn.model_parallel.layers import ColumnParallelLinear, ParallelEmbedding, RowParallelLinear
  from torch import nn
label_desc: Use image node_id 2 for calling a global function with example usage: reshape_for_broadcast(freqs_cis, x) and returns: freqs_cis