code: def __init__(self, config):
      super(BertIntermediate, self).__init__()
      self.dense = quantized_linear_setup(
          config,
          "ffn_intermediate",
          config.hidden_size,
          config.intermediate_size,
      )
      if isinstance(config.hidden_act, str) or (
          sys.version_info[0] == 2
          and isinstance(config.hidden_act, str)
      ):  # noqa: F821
          self.intermediate_act_fn = ACT2FN[config.hidden_act]
      else:
          self.intermediate_act_fn = config.hidden_act
  
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 QuantizedBertIntermediate object from inherited base classes: BertIntermediate with example: obj = QuantizedBertIntermediate(config)