class_docstr: Permutes input vector in a random but fixed way.
  
  Args:
      dim (list[tuple[int]]): Dimension of the input vector.
      seed (float | None=None): Seed for the random permutation.
code: def __init__(
      self, dims_in: list[tuple[int]], seed: float | None = None
  ) -> None:
      super().__init__(dims_in)
      self.n_inputs: int = len(dims_in)
      self.in_channels = [dims_in[i][0] for i in range(self.n_inputs)]
  
      np.random.seed(seed)
      perm, perm_inv = self.get_random_perm(0)
      self.perm = [perm]  # stores the random order of channels
      self.perm_inv = [
          perm_inv
      ]  # stores the inverse mapping to recover the original order of channels
  
      for i in range(1, self.n_inputs):
          perm, perm_inv = self.get_random_perm(i)
          self.perm.append(perm)
          self.perm_inv.append(perm_inv)
  
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 ParallelPermute object from inherited base classes: InvertibleModule with example: obj = ParallelPermute(dims_in, seed)