code: def get_feature_extractor(
      backbone, input_size: Tuple[int, int] = (256, 256)
  ):
      """
      Get feature extractor. Currently, is restricted to AVAILABLE_EXTRACTORS.
      Args:
          backbone (str): Backbone name.
          input_size (tuple[int, int]): Input size.
  
      Returns:
          FeatureExtractorInterface: Feature extractor.
      """
      assert (
          backbone in AVAILABLE_EXTRACTORS
      ), f"Feature extractor must be one of {AVAILABLE_EXTRACTORS}."
      if backbone in ["resnet18", "wide_resnet50_2"]:
          return FeatureExtractor(
              backbone,
              input_size,
              layers=["layer1", "layer2", "layer3"],
          )
      elif backbone == "mcait":
          return MCaitFeatureExtractor()
      raise ValueError(
          "`backbone` must be one of `[mcait, resnet18, wide_resnet50_2]`. These are the only feature extractors tested. "
          "It does not mean that other feature extractors will not work."
      )
  
file_path: anomalib/src/anomalib/models/uflow/feature_extraction.py
imports: from typing import Tuple
  import timm
  import torch
  import torch.nn.functional
  from torch import nn
  from anomalib.models.components.feature_extractors import TimmFeatureExtractor
label_desc: Use image node_id 1 for calling a global function with example usage: get_feature_extractor(backbone, input_size) and returns: FeatureExtractor, MCaitFeatureExtractor