code: def dequantize(
      self, tensor: Tensor, quant_scale: Tensor, quant_min: Tensor
  ) -> Tensor:
      # Use customized CUDA quantization kernel if possible.
      if (
          self.config["group_size"] % 8 == 0
          and (
              self.config["num_bits"] == 4
              or self.config["num_bits"] == 8
          )
          and self.config["group_dim"] == len(tensor.shape) - 1
          and self.dtype == torch.float16
          and device == "cuda"
      ):
          last_dimension_size = self.config["group_size"]
          if self.config["num_bits"] == 4:
              last_dimension_size = last_dimension_size // 2
              quantized_tensor = get_quantizer_cuda_module().dequantize_int4_to_half_experimental(
                  tensor.reshape(-1, last_dimension_size),
                  quant_scale,
                  quant_min,
                  tensor.numel() // last_dimension_size,
                  self.config["group_size"],
              )
              shape = list(tensor.shape)
              shape[-1] = shape[-1] * 2
          elif self.config["num_bits"] == 8:
              # last_dimension_size = last_dimension_size // 2
              quantized_tensor = get_quantizer_cuda_module().dequantize_int8_to_half_experimental(
                  tensor.reshape(-1, last_dimension_size),
                  quant_scale,
                  quant_min,
                  tensor.numel() // last_dimension_size,
                  self.config["group_size"],
              )
              shape = list(tensor.shape)
  
          return quantized_tensor.reshape(shape)
  
      if self.config["num_bits"] == 4:
          tensor = self._decompress_uint4_to_uint8(tensor)
      elif self.config["num_bits"] != 8:
          assert False, "Unsupported quantization bits {}".format(
              self.config["num_bits"]
          )
  
      shape = tensor.shape
      num_groups = (
          shape[self.config["group_dim"]] // self.config["group_size"]
      )
      new_shape = (
          shape[: self.config["group_dim"]]
          + (num_groups, self.config["group_size"])
          + shape[self.config["group_dim"] + 1 :]
      )
      tensor = tensor.view(new_shape)
  
      dequantized_tensor = self._dequantize_int8(
          tensor, quant_scale, quant_min
      ).view(shape)
      return dequantized_tensor
  
imports: import torch
  import deepspeed
  from torch import Tensor
  from typing import Tuple
  import torch.nn
  from typing import Dict, Callable, Union
  from deepspeed.accelerator import get_accelerator
  import functools
label_desc: Use image node_id 2 for calling the DeQuantizer obj's underlying member method code with example usage: obj.dequantize(tensor, quant_scale, quant_min) and returns: dequantized_tensor, quantized_tensor