code: def _ffcv_infer_encoder(
      value, ffcv_parameters: "FFCVParameters"
  ) -> Optional["Field"]:
      """
      Infers the field encoder definition from a given example.
  
      :param value: The example obtained from the dataset.
      :param ffcv_parameters: The additional parameters passed
          to the :func:`enable_ffcv` function.
      :return: A :class:`Field` instance or None if it cannot be
          inferred.
      """
      from ffcv.fields import (
          IntField,
          FloatField,
          NDArrayField,
          TorchTensorField,
      )
  
      if isinstance(value, int):
          return IntField()
  
      if isinstance(value, float):
          return FloatField()
  
      if isinstance(value, np.ndarray):
          return NDArrayField(value.dtype, shape=value.shape)
  
      if isinstance(value, Tensor):
          return TorchTensorField(value.dtype, shape=value.shape)
  
      if isinstance(value, Image):
          return _image_encoder(ffcv_parameters)
  
      return None
  
file_path: avalanche/avalanche/benchmarks/utils/ffcv_support/ffcv_support_internals.py
imports: from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Sequence, Union
  from collections import OrderedDict
  import numpy
  from torch import Tensor
  from PIL.Image import Image
  from ffcv.fields import TorchTensorField
  from ffcv.fields.decoders import IntDecoder, FloatDecoder, NDArrayDecoder, SimpleRGBImageDecoder
label_desc: Use image node_id 2 for calling a global function with example usage: _ffcv_infer_encoder(value, ffcv_parameters) and returns: None, IntField, FloatField, NDArrayField, TorchTensorField, _image_encoder