code: def forward(
      self,
      hidden_states: torch.Tensor,
      position_bias: torch.Tensor,
      attention_mask: torch.Tensor,
      layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
      use_cache: bool = False,
      output_attentions: bool = False,
  ):
      # hidden_states: [batch_size, seq_length, hidden_size]
      # Layer norm at the beginning of the transformer layer.
      layernorm_output = self.norm_1(hidden_states)
  
      residual = hidden_states
  
      # Self attention.
      attn_outputs, attn_weights, past_key_value = self.attn(
          layernorm_output,
          position_bias=position_bias,
          attention_mask=attention_mask,
          past_key_value=layer_past,
      )
  
      hidden_states = self.resid_attn_dropout(attn_outputs) + residual
  
      layernorm_output = self.norm_2(hidden_states)
  
      # Get residual
      residual = hidden_states
  
      # MLP.
      output = self.ffn(layernorm_output, residual)
      outputs = (output,)
  
      if use_cache:
          outputs += (past_key_value,)
  
      if output_attentions:
          outputs += (attn_weights,)
  
      return outputs
  
imports: import math
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
  from torch.nn import functional
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
  from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_mpt import MptConfig
label_desc: Use image node_id 2 for calling the MptBlock obj's underlying member method code with example usage: obj.forward(hidden_states, position_bias, attention_mask, layer_past, use_cache, output_attentions) and returns: outputs