code: def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
      """
      Reshape frequency tensor for broadcasting it with another tensor.
  
      This function reshapes the frequency tensor to have the same shape as the target tensor 'x'
      for the purpose of broadcasting the frequency tensor during element-wise operations.
  
      Args:
          freqs_cis (torch.Tensor): Frequency tensor to be reshaped.
          x (torch.Tensor): Target tensor for broadcasting compatibility.
  
      Returns:
          torch.Tensor: Reshaped frequency tensor.
  
      Raises:
          AssertionError: If the frequency tensor doesn't match the expected shape.
          AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions.
      """
      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 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