code: def get_partitions(
      edge_index: Union[Tensor, SparseTensor],
      num_partitions: int,
      indices: Tensor,
      num_nodes: int,
      output_weight: Optional[float] = None,
  ) -> List[Tensor]:
      assert isinstance(
          edge_index, (torch.LongTensor, SparseTensor)
      ), f"Unsupported edge_index type {type(edge_index)}"
      if isinstance(edge_index, torch.LongTensor):
          edge_index = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(num_nodes, num_nodes)
          )
  
      if output_weight is not None and output_weight != 1:
          node_weight = torch.ones(num_nodes)
          node_weight[indices] = output_weight
      else:
          node_weight = None
  
      _, partptr, perm = edge_index.partition(
          num_parts=num_partitions,
          recursive=False,
          weighted=False,
          node_weight=node_weight,
      )
  
      partitions = []
      for i in range(len(partptr) - 1):
          partitions.append(perm[partptr[i] : partptr[i + 1]])
  
      return partitions
  
imports: import logging
  import math
  from typing import Callable, Iterator, List, NamedTuple, Optional, Tuple, Union
  import numpy
  import scipy.sparse
  import torch
  from torch import Tensor
  from tqdm import tqdm
  from torch_geometric.data import Data
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import get_ppr, is_undirected, subgraph
label_desc: Use image node_id 1 for calling a global function with example usage: get_partitions(edge_index, num_partitions, indices, num_nodes, output_weight) and returns: partitions