code: def __init__(self, layer_id: int, config: OPTConfig, weights):
      super().__init__()
      self.process_group = weights.process_group
      self.hidden_size = config.hidden_size
      prefix = f"model.decoder.layers.{layer_id}"
      self.self_attn = OPTAttention(
          config,
          prefix=f"{prefix}.self_attn",
          weights=weights,
          is_decoder=True,
          bias=config.enable_bias,
      )
      self.do_layer_norm_before = config.do_layer_norm_before
      self.dropout = config.dropout
      self.activation_fn = ACT2FN[config.activation_function]
  
      self.self_attn_layer_norm = nn.LayerNorm.load(
          prefix=f"{prefix}.self_attn_layer_norm",
          weights=weights,
          eps=EPS,
      )
      self.fc1 = TensorParallelColumnLinear.load(
          config,
          prefix=f"{prefix}.fc1",
          weights=weights,
          bias=config.enable_bias,
      )
      self.fc2 = TensorParallelRowLinear.load(
          config,
          prefix=f"{prefix}.fc2",
          weights=weights,
          bias=config.enable_bias,
      )
      self.final_layer_norm = nn.LayerNorm.load(
          prefix=f"{prefix}.final_layer_norm", weights=weights, eps=EPS
      )
  
imports: import random
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers import OPTConfig
  from text_generation_server.utils.layers import FastLinear, TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, TensorParallelHead
label_desc: Use image node_id 1 to create a new OPTDecoderLayer object from inherited base classes: nn with example: obj = OPTDecoderLayer(layer_id, config, weights)