code: def forward(
      self,
      x: torch.Tensor,
      past_key_value: Optional[Tuple[torch.Tensor]] = None,
      attn_bias: Optional[torch.Tensor] = None,
      attention_mask: Optional[torch.ByteTensor] = None,
      is_causal: bool = True,
  ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor]]]:
      a = self.norm_1(x)
      (b, attn_weights, past_key_value) = self.attn(
          a,
          past_key_value=past_key_value,
          attn_bias=attn_bias,
          attention_mask=attention_mask,
          is_causal=is_causal,
      )
      x = x + self.resid_attn_dropout(b)
      m = self.norm_2(x)
      n = self.ffn(m)
      x = x + self.resid_ffn_dropout(n)
      return (x, attn_weights, past_key_value)
  
imports: import math
  import os
  import warnings
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from einops import rearrange
  from packaging import version
  from text_generation_server.utils.layers import TensorParallelEmbedding, TensorParallelColumnLinear, TensorParallelRowLinear, TensorParallelHead, get_linear
label_desc: Use image node_id 2 for calling the MPTBlock obj's underlying member method code with example usage: obj.forward(x, past_key_value, attn_bias, attention_mask, is_causal) and returns: x,  attn_weights,  past_key_value