code: def forward(
      self,
      hidden_states,
      attention_mask=None,
      position_bias=None,
      encoder_hidden_states=None,
      encoder_attention_mask=None,
      encoder_decoder_position_bias=None,
      layer_head_mask=None,
      cross_attn_layer_head_mask=None,
      past_key_value=None,
      use_cache=False,
      output_attentions=False,
      return_dict=True,
  ):
      if past_key_value is not None:
          if not self.is_decoder:
              logger.warning(
                  "`past_key_values` is passed to the encoder. Please make sure this is intended."
              )
          expected_num_past_key_values = (
              2 if encoder_hidden_states is None else 4
          )
  
          if len(past_key_value) != expected_num_past_key_values:
              raise ValueError(
                  f"There should be {expected_num_past_key_values} past states. "
                  f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}"
                  f"Got {len(past_key_value)} past key / value states"
              )
  
          self_attn_past_key_value = past_key_value[:2]
          cross_attn_past_key_value = past_key_value[2:]
      else:
          self_attn_past_key_value, cross_attn_past_key_value = (
              None,
              None,
          )
  
      self_attention_outputs = self.layer[0](
          hidden_states,
          attention_mask=attention_mask,
          position_bias=position_bias,
          layer_head_mask=layer_head_mask,
          past_key_value=self_attn_past_key_value,
          use_cache=use_cache,
          output_attentions=output_attentions,
      )
      hidden_states, present_key_value_state = self_attention_outputs[
          :2
      ]
      attention_outputs = self_attention_outputs[
          2:
      ]  # Keep self-attention outputs and relative position weights
  
      # clamp inf values to enable fp16 training
      if hidden_states.dtype == torch.float16:
          clamp_value = torch.where(
              torch.isinf(hidden_states).any(),
              torch.finfo(hidden_states.dtype).max - 1000,
              torch.finfo(hidden_states.dtype).max,
          )
          hidden_states = torch.clamp(
              hidden_states, min=-clamp_value, max=clamp_value
          )
  
      do_cross_attention = (
          self.is_decoder and encoder_hidden_states is not None
      )
      if do_cross_attention:
          # the actual query length is unknown for cross attention
          # if using past key value states. Need to inject it here
          if present_key_value_state is not None:
              query_length = present_key_value_state[0].shape[2]
          else:
              query_length = None
  
          cross_attention_outputs = self.layer[1](
              hidden_states,
              key_value_states=encoder_hidden_states,
              attention_mask=encoder_attention_mask,
              position_bias=encoder_decoder_position_bias,
              layer_head_mask=cross_attn_layer_head_mask,
              past_key_value=cross_attn_past_key_value,
              query_length=query_length,
              use_cache=use_cache,
              output_attentions=output_attentions,
          )
          hidden_states = cross_attention_outputs[0]
  
          # clamp inf values to enable fp16 training
          if hidden_states.dtype == torch.float16:
              clamp_value = torch.where(
                  torch.isinf(hidden_states).any(),
                  torch.finfo(hidden_states.dtype).max - 1000,
                  torch.finfo(hidden_states.dtype).max,
              )
              hidden_states = torch.clamp(
                  hidden_states, min=-clamp_value, max=clamp_value
              )
  
          # Combine self attn and cross attn key value states
          if present_key_value_state is not None:
              present_key_value_state = (
                  present_key_value_state + cross_attention_outputs[1]
              )
  
          # Keep cross-attention outputs and relative position weights
          attention_outputs = (
              attention_outputs + cross_attention_outputs[2:]
          )
  
      # Apply Feed Forward layer
      hidden_states = self.layer[-1](hidden_states)
  
      # clamp inf values to enable fp16 training
      if hidden_states.dtype == torch.float16:
          clamp_value = torch.where(
              torch.isinf(hidden_states).any(),
              torch.finfo(hidden_states.dtype).max - 1000,
              torch.finfo(hidden_states.dtype).max,
          )
          hidden_states = torch.clamp(
              hidden_states, min=-clamp_value, max=clamp_value
          )
  
      outputs = (hidden_states,)
  
      if use_cache:
          outputs = (
              outputs + (present_key_value_state,) + attention_outputs
          )
      else:
          outputs = outputs + attention_outputs
  
      return outputs
  
imports: import copy
  import math
  import warnings
  from typing import Optional, Tuple, Union
  from loguru import logger
  import torch
  import torch.distributed
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput
  from transformers.modeling_utils import PreTrainedModel
  from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
  from transformers.utils import is_torch_fx_proxy
  from transformers import T5Config
  from text_generation_server.utils.layers import TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, TensorParallelHead
label_desc: Use image node_id 2 for calling the T5Block obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, position_bias, encoder_hidden_states, encoder_attention_mask, encoder_decoder_position_bias, layer_head_mask, cross_attn_layer_head_mask, past_key_value, use_cache, output_attentions, return_dict) and returns: outputs