code: def __init__(self, config: T5Config, prefix, weights):
      super().__init__()
      self.wi = TensorParallelColumnLinear.load(
          config, prefix=f"{prefix}.wi", weights=weights, bias=False
      )
  
      ### XXX: T5 models do not handle well both f16 and quantization.
      ### Overidding specifically this layer for that reason.
      ### https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py#L316
      ### https://github.com/huggingface/transformers/issues/20287
      _q = config.quantize
      _dtype = weights.dtype
      weights.dtype = torch.float32
      config.quantize = None
      self.wo_cast = (torch.float32, _dtype)
      self.wo = TensorParallelRowLinear.load(
          config, prefix=f"{prefix}.wo", weights=weights, bias=False
      )
      weights.dtype = _dtype
      config.quantize = _q
  
      self.dropout = nn.Dropout(config.dropout_rate)
      self.act = (
          ACT2FN[config.dense_act_fn]
          if "gelu" not in config.dense_act_fn
          else lambda x: torch.nn.functional.gelu(x, approximate="tanh")
      )
  
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 1 to create a new T5DenseActDense object from inherited base classes: nn with example: obj = T5DenseActDense(config, prefix, weights)