class_docstr: Embedding layer with quantization aware training capability
class_docstr_tok: ["Embedding","layer","with","quantization","aware","training","capability"]
docstr: forward to be used during inference
code: def inference_quantized_forward(self, input):
      """forward to be used during inference"""
      assert (
          not self.training
      ), "should only be called when not training"
      q_embeddings = F.embedding(
          input,
          self.quantized_weight,
          self.padding_idx,
          self.max_norm,
          self.norm_type,
          self.scale_grad_by_freq,
          self.sparse,
      )
      return dequantize(q_embeddings, self.weight_scale)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from enum import Enum, auto
  import logging
  from abc import ABC, abstractmethod
  import torch
  from torch import nn
  from torch.nn import functional
  from nlp_architect.common import Config
label_desc: Use image node_id 2 for calling the QuantizedEmbedding obj's underlying member method code with example usage: obj.inference_quantized_forward(input) and returns: dequantize