code: def quantize_feature(
      module: torch.nn.Module, inputs: Tuple[torch.Tensor, ...]
  ) -> Tuple[torch.Tensor, ...]:
      return tuple(
          [
              input.half()
              if isinstance(input, torch.Tensor)
              and input.dtype in [torch.float32, torch.float64]
              else input
              for input in inputs
          ]
      )
  
imports: import abc
  import json
  from dataclasses import asdict, dataclass
  from typing import Any, Dict, List, Optional, Tuple, Type
  import torch
  import torch.nn
  import torch.quantization
  import torchrec
  import torchrec.quant
  from torchrec.modules.embedding_configs import QuantConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollectionInterface, EmbeddingCollectionInterface
label_desc: Use image node_id 2 for calling a global function with example usage: quantize_feature(module, inputs) and returns: tuple