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.
docstr: Returns the output dimensions of the module.
docstr_tok: ["Returns","the","output","dimensions","of","the","module","."]
code: def output_dims(
      self, input_dims: list[tuple[int]]
  ) -> list[tuple[int]]:
      """Returns the output dimensions of the module."""
      return input_dims
  
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 4 for calling the ParallelPermute obj's underlying member method code with example usage: obj.output_dims(input_dims) and returns: input_dims