code: def quantized_embedding_setup(config, name, *args, **kwargs):
      """
      Get QuantizedEmbedding layer according to config params
      """
      try:
          quant_config = QuantizationConfig.from_dict(
              getattr(config, name)
          )
          embedding = QuantizedEmbedding.from_config(
              *args, **kwargs, config=quant_config
          )
      except AttributeError:
          embedding = nn.Embedding(*args, **kwargs)
      return embedding
  
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 2 for calling a global function with example usage: quantized_embedding_setup(config, name) and returns: embedding