class_docstr: An implementation of an in-memory heterogeneous layer-wise sampler
  user by :class:`~torch_geometric.loader.HGTLoader`.
class_docstr_tok: ["An","implementation","of","an","in-memory","heterogeneous","layer-wise","sampler","user","by",":","class",":","`","~torch_geometric.loader.HGTLoader","`","."]
code: def edge_permutation(
      self,
  ) -> Union[OptTensor, Dict[EdgeType, OptTensor]]:
      return self.perm
  
file_path: pytorch_geometric/torch_geometric/sampler/hgt_sampler.py
imports: from typing import Dict, List, Union
  import torch
  from torch_geometric.data import Data, HeteroData
  from torch_geometric.sampler import BaseSampler, HeteroSamplerOutput, NodeSamplerInput
  from torch_geometric.sampler.utils import remap_keys, to_hetero_csc
  from torch_geometric.typing import WITH_TORCH_SPARSE, EdgeType, NodeType, OptTensor
label_desc: Use image node_id 3 for calling the HGTSampler obj's underlying member method code with example usage: obj.edge_permutation() and returns: self