class_docstr: Cross scale coupling layer.
  
  Args:
      input_dims (tuple[int, int, int]): Input dimensions of the module.
      n_coupling_blocks (int): Number of coupling blocks.
      clamp (float): Clamp value for the inputs.
      corss_conv_hidden_channels (int): Number of hidden channels in the cross convolution.
code: def __init__(
      self,
      input_dims: tuple[int, int, int],
      n_coupling_blocks: int,
      clamp: float,
      cross_conv_hidden_channels: int,
  ) -> None:
      super().__init__()
      self.input_dims = input_dims
      self.n_coupling_blocks = n_coupling_blocks
      self.kernel_sizes = [3] * (n_coupling_blocks - 1) + [5]
      self.clamp = clamp
      self.cross_conv_hidden_channels = cross_conv_hidden_channels
      self.graph = self._create_graph()
  
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 CrossScaleFlow object from inherited base classes: nn with example: obj = CrossScaleFlow(input_dims, n_coupling_blocks, clamp, cross_conv_hidden_channels)