code: def forward(
      self,
      x: Tensor,
      input_pos: Tensor,
      freqs_cis: Tensor,
      mask: Tensor,
  ) -> Tensor:
      h = x + self.attention(
          self.attention_norm(x), freqs_cis, mask, input_pos
      )
      out = h + self.feed_forward(self.ffn_norm(h))
      return out
  
imports: from dataclasses import dataclass
  from typing import Optional
  import torch
  import torch.nn
  from torch import Tensor
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the TransformerBlock obj's underlying member method code with example usage: obj.forward(x, input_pos, freqs_cis, mask) and returns: out