class_docstr: Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection.
  
  Args:
      input_size (tuple[int, int]): Size of the model input.
      n_coupling_blocks (int): Number of coupling blocks in the model.
      cross_conv_hidden_channels (int): Number of hidden channels in the cross convolution.
      clamp (int): Clamp value for glow layer.
      num_channels (int): Number of channels in the model.
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code: def __init__(
      self,
      input_size: tuple[int, int],
      cross_conv_hidden_channels: int,
      n_coupling_blocks: int,
      clamp: int,
      num_channels: int,
  ) -> None:
      super().__init__()
      self.model: CsFlowModel = CsFlowModel(
          input_size=input_size,
          cross_conv_hidden_channels=cross_conv_hidden_channels,
          n_coupling_blocks=n_coupling_blocks,
          clamp=clamp,
          num_channels=num_channels,
      )
      self.loss = CsFlowLoss()
  
imports: from __future__ import annotations
  import logging
  import torch
  from omegaconf import DictConfig, ListConfig
  from pytorch_lightning.callbacks import Callback, EarlyStopping
  from pytorch_lightning.utilities.types import STEP_OUTPUT
  from torch import Tensor
  from anomalib.models.components import AnomalyModule
  from .loss import CsFlowLoss
  from .torch_model import CsFlowModel
label_desc: Use image node_id 1 to create a new Csflow object from inherited base classes: AnomalyModule with example: obj = Csflow(input_size, cross_conv_hidden_channels, n_coupling_blocks, clamp, num_channels)