class_docstr: Multi-scale feature extractor.
  
  Uses 36th layer of EfficientNet-B5 to extract features.
  
  Args:
      n_scales (int): Number of scales for input image.
      input_size (tuple[int, int]): Size of input image.
code: def __init__(
      self, n_scales: int, input_size: tuple[int, int]
  ) -> None:
      super().__init__()
  
      self.n_scales = n_scales
      self.input_size = input_size
      self.feature_extractor = TorchFXFeatureExtractor(
          backbone="efficientnet_b5",
          weights=EfficientNet_B5_Weights.DEFAULT,
          return_nodes=["features.6.8"],
      )
  
imports: from __future__ import annotations
  from math import exp
  import numpy
  import torch
  import torch.nn.functional
  from FrEIA.framework import GraphINN, InputNode, Node, OutputNode
  from FrEIA.modules import InvertibleModule
  from torch import Tensor, nn
  from torchvision.models.efficientnet import EfficientNet_B5_Weights
  from anomalib.models.components.feature_extractors import TorchFXFeatureExtractor
  from .anomaly_map import AnomalyMapGenerator, AnomalyMapMode
label_desc: Use image node_id 1 to create a new MultiScaleFeatureExtractor object from inherited base classes: nn with example: obj = MultiScaleFeatureExtractor(n_scales, input_size)