code: def forward(self, input: Tensor) -> Tensor:
      quantized_weight, quant_scale, quant_min = self.weight.deconcat(
          self.weight
      )
      temp_dequantized_weight = self.weight.dequantizer.dequantize(
          quantized_weight.view(torch.uint8), quant_scale, quant_min
      )
  
      return F.embedding(
          input,
          temp_dequantized_weight,
          self.padding_idx,
          self.max_norm,
          self.norm_type,
          self.scale_grad_by_freq,
          self.sparse,
      )
  
file_path: DeepSpeed/deepspeed/inference/quantization/layers.py
imports: import torch
  from torch import nn
  from torch import Tensor
  from torch.nn import functional
  from .utils import Quantizer, DeQuantizer, concat_to_compat_param
  from typing import Tuple, Callable, Dict
  from deepspeed.runtime.zero import register_external_parameter
label_desc: Use image node_id 2 for calling the QuantizedEmbedding obj's underlying member method code with example usage: obj.forward(input) and returns: F