docstr: Initialize the weights.
code: def init_weights(self, module):
      """Initialize the weights."""
      if isinstance(
          module,
          (
              nn.Linear,
              nn.Embedding,
              QuantizedLinear,
              QuantizedEmbedding,
          ),
      ):
          module.weight.data.normal_(
              mean=0.0, std=self.config.initializer_range
          )
      elif isinstance(module, BertLayerNorm):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0)
      if isinstance(module, nn.Linear) and module.bias is not None:
          module.bias.data.zero_()
  
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 for calling the QuantizedBertPreTrainedModel obj's underlying member method code with example usage: obj.init_weights(module) without return types