docstr: Args:
      input_size (tuple[int, int]): Input image size.
      flow_steps (int): Number of flow steps.
      backbone (str): Backbone name.
      affine_clamp (float): Affine clamp.
      affine_subnet_channels_ratio (float): Affine subnet channels ratio.
      permute_soft (bool): Whether to use soft permutation.
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code: def __init__(
      self,
      input_size: tuple[int, int] = (448, 448),
      flow_steps: int = 4,
      backbone: str = "mcait",
      affine_clamp: float = 2.0,
      affine_subnet_channels_ratio: float = 1.0,
      permute_soft: bool = False,
  ) -> None:
      """
      Args:
          input_size (tuple[int, int]): Input image size.
          flow_steps (int): Number of flow steps.
          backbone (str): Backbone name.
          affine_clamp (float): Affine clamp.
          affine_subnet_channels_ratio (float): Affine subnet channels ratio.
          permute_soft (bool): Whether to use soft permutation.
      """
      super().__init__()
  
      self.input_size = input_size
      self.affine_clamp = affine_clamp
      self.affine_subnet_channels_ratio = affine_subnet_channels_ratio
      self.permute_soft = permute_soft
  
      self.feature_extractor = get_feature_extractor(
          backbone, input_size
      )
      self.flow = self.build_flow(flow_steps)
      self.anomaly_map_generator = AnomalyMapGenerator(input_size)
  
file_path: anomalib/src/anomalib/models/uflow/torch_model.py
imports: import torch.nn
  from FrEIA import framework
  from FrEIA import modules
  from anomalib.models.components.flow import AllInOneBlock
  from .anomaly_map import AnomalyMapGenerator
  from .feature_extraction import get_feature_extractor
label_desc: Use image node_id 1 to create a new UflowModel object from inherited base classes: nn with example: obj = UflowModel(input_size, flow_steps, backbone, affine_clamp, affine_subnet_channels_ratio, permute_soft)