code: def __init__(self, config):
      super(BertEncoder, self).__init__()
      self.output_attentions = config.output_attentions
      self.output_hidden_states = config.output_hidden_states
      self.layer = nn.ModuleList(
          [
              QuantizedBertLayer(config)
              for _ in range(config.num_hidden_layers)
          ]
      )
  
imports: import logging
  import os
  import sys
  import torch
  from torch import nn
  from transformers.modeling_bert import ACT2FN, BertAttention, BertConfig, BertEmbeddings, BertEncoder, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertIntermediate, BertLayer, BertLayerNorm, BertModel, BertOutput, BertPooler, BertPreTrainedModel, BertSelfAttention, BertSelfOutput
  from nlp_architect.models.pretrained_models import S3_PREFIX
  from nlp_architect.nn.torch.quantization import QuantizationConfig, QuantizedEmbedding, QuantizedLayer, QuantizedLinear
label_desc: Use image node_id 1 to create a new QuantizedBertEncoder object from inherited base classes: BertEncoder with example: obj = QuantizedBertEncoder(config)