code: def forward(
      self,
      hidden_states: torch.Tensor,
      alibi: torch.Tensor,
      attention_mask: torch.Tensor,
      layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
      head_mask: Optional[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.input_layernorm(hidden_states)
  
      # Layer norm post the self attention.
      if self.apply_residual_connection_post_layernorm:
          residual = layernorm_output
      else:
          residual = hidden_states
  
      # Self attention.
      attn_outputs = self.self_attention(
          layernorm_output,
          residual,
          layer_past=layer_past,
          attention_mask=attention_mask,
          alibi=alibi,
          head_mask=head_mask,
          use_cache=use_cache,
          output_attentions=output_attentions,
      )
  
      attention_output = attn_outputs[0]
  
      outputs = attn_outputs[1:]
  
      layernorm_output = self.post_attention_layernorm(attention_output)
  
      # Get residual
      if self.apply_residual_connection_post_layernorm:
          residual = layernorm_output
      else:
          residual = attention_output
  
      # MLP.
      output = self.mlp(layernorm_output, residual)
  
      if use_cache:
          outputs = (output,) + outputs
      else:
          outputs = (output,) + outputs[1:]
  
      return outputs
  
imports: import math
  import os
  import warnings
  from typing import Optional, Tuple, Union
  import torch
  import torch.distributed
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import LayerNorm
  from torch.nn import functional
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
  from transformers import BloomConfig, PreTrainedModel
  from text_generation_server.utils.layers import TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, TensorParallelHead
label_desc: Use image node_id 2 for calling the BloomBlock obj's underlying member method code with example usage: obj.forward(hidden_states, alibi, attention_mask, layer_past, head_mask, use_cache, output_attentions) and returns: outputs