code: def __init__(self, config):
      super(BertLayer, self).__init__()
      self.attention = QuantizedBertAttention(config)
      self.is_decoder = config.is_decoder
      if self.is_decoder:
          logger.warning(
              "Using QuantizedBertLayer as decoder was not tested."
          )
          self.crossattention = QuantizedBertAttention(config)
      self.intermediate = QuantizedBertIntermediate(config)
      self.output = QuantizedBertOutput(config)
  
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 QuantizedBertLayer object from inherited base classes: BertLayer with example: obj = QuantizedBertLayer(config)