code: def __init__(self, config):
      # we only want BertForQuestionAnswering init to run to avoid unnecessary
      # initializations
      super(BertForQuestionAnswering, self).__init__(config)
      self.num_labels = config.num_labels
  
      self.bert = QuantizedBertModel(config)
      self.qa_outputs = quantized_linear_setup(
          config, "head", config.hidden_size, config.num_labels
      )
  
      self.apply(self.init_weights)
  
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 QuantizedBertForQuestionAnswering object from inherited base classes: QuantizedBertPreTrainedModel, BertForQuestionAnswering with example: obj = QuantizedBertForQuestionAnswering(config)