code: def quantize(self, tensor: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
      assert (
          tensor.shape[self.config["group_dim"]]
          % self.config["group_size"]
          == 0
      ), f"Tensor shape: {tensor.shape} quantization config {self.config}"
  
      tensor = torch.clone(tensor)
  
      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)
  
      quantized_tensor, scale, min_value = self._quantize_int8(tensor)
      quantized_tensor = quantized_tensor.view(shape)
  
      if self.config["num_bits"] == 4:
          return (
              self._compress_uint8_to_uint4(quantized_tensor),
              scale,
              min_value,
          )
      if self.config["num_bits"] == 8:
          return quantized_tensor, scale, min_value
  
      assert False, "Unsupported quantization bits {}".format(
          self.config["num_bits"]
      )
  
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 Quantizer obj's underlying member method code with example usage: obj.quantize(tensor) and returns: self,  scale,  min_value, quantized_tensor,  scale,  min_value