docstr: Perform a forward pass through the TransformerBlock.
  
  Args:
      x (torch.Tensor): Input tensor.
      start_pos (int): Starting position for attention caching.
      freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies.
      mask (torch.Tensor, optional): Masking tensor for attention. Defaults to None.
  
  Returns:
      torch.Tensor: Output tensor after applying attention and feedforward layers.
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code: def forward(
      self,
      x: torch.Tensor,
      start_pos: int,
      freqs_cis: torch.Tensor,
      mask: Optional[torch.Tensor],
  ):
      """
      Perform a forward pass through the TransformerBlock.
  
      Args:
          x (torch.Tensor): Input tensor.
          start_pos (int): Starting position for attention caching.
          freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies.
          mask (torch.Tensor, optional): Masking tensor for attention. Defaults to None.
  
      Returns:
          torch.Tensor: Output tensor after applying attention and feedforward layers.
  
      """
      h = x + self.attention.forward(
          self.attention_norm(x), start_pos, freqs_cis, mask
      )
      out = h + self.feed_forward.forward(self.ffn_norm(h))
      return out
  
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 the TransformerBlock obj's underlying member method code with example usage: obj.forward(x, start_pos, freqs_cis, mask) and returns: out