code: def __init__(self, config, weights, biases):
      super(DSEncoder, self).__init__()
      self.FinalLayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
      self.layer = nn.ModuleList(
          [
              copy.deepcopy(
                  DeepSpeedTransformerLayer(config, weights, biases)
              )
              for _ in range(config.num_hidden_layers)
          ]
      )
      self.grads = []
      self.pre_or_post = config.pre_layer_norm
  
imports: import numpy
  import torch
  import pytest
  import random
  import copy
  import os
  from torch import nn
  from deepspeed import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig
  from deepspeed.accelerator import get_accelerator
  from unit.modeling import BertConfig, BertLayerNorm, BertEncoder
  from unit.modelingpreln import BertEncoder
  from unit.common import DistributedTest, is_rocm_pytorch
label_desc: Use image node_id 1 to create a new DSEncoder object from inherited base classes: nn with example: obj = DSEncoder(config, weights, biases)