code: def __init__(self, config):
      super(BertPooler, self).__init__()
      self.dense = quantized_linear_setup(
          config, "pooler", config.hidden_size, config.hidden_size
      )
      self.activation = nn.Tanh()
  
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 QuantizedBertPooler object from inherited base classes: BertPooler with example: obj = QuantizedBertPooler(config)