code: def k_hop_subgraph(
      node_idx,
      num_hops,
      edge_index,
      relabel_nodes=False,
      num_nodes=None,
      flow="source_to_target",
  ):
      r"""Computes the :math:`k`-hop subgraph of :obj:`edge_index` around node
      :attr:`node_idx`.
      It returns (1) the nodes involved in the subgraph, (2) the filtered
      :obj:`edge_index` connectivity, (3) the mapping from node indices in
      :obj:`node_idx` to their new location, and (4) the edge mask indicating
      which edges were preserved.
      Args:
          node_idx (int, list, tuple or :obj:`torch.Tensor`): The central
              node(s).
          num_hops: (int): The number of hops :math:`k`.
          edge_index (LongTensor): The edge indices.
          relabel_nodes (bool, optional): If set to :obj:`True`, the resulting
              :obj:`edge_index` will be relabeled to hold consecutive indices
              starting from zero. (default: :obj:`False`)
          num_nodes (int, optional): The number of nodes, *i.e.*
              :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)
          flow (string, optional): The flow direction of :math:`k`-hop
              aggregation (:obj:`"source_to_target"` or
              :obj:`"target_to_source"`). (default: :obj:`"source_to_target"`)
      :rtype: (:class:`LongTensor`, :class:`LongTensor`, :class:`LongTensor`,
               :class:`BoolTensor`)
      """
  
      assert flow in ["source_to_target", "target_to_source"]
      if flow == "target_to_source":
          row, col = edge_index
      else:
          col, row = edge_index
  
      node_mask = row.new_empty(num_nodes, dtype=torch.bool)
      edge_mask = row.new_empty(row.size(0), dtype=torch.bool)
  
      if isinstance(node_idx, (int, list, tuple)):
          node_idx = torch.tensor(
              [node_idx], device=row.device
          ).flatten()
      else:
          node_idx = node_idx.to(row.device)
  
      subsets = [node_idx]
  
      for _ in range(num_hops):
          node_mask.fill_(False)
          node_mask[subsets[-1]] = True
          torch.index_select(node_mask, 0, row, out=edge_mask)
          subsets.append(col[edge_mask])
  
      subset, inv = torch.cat(subsets).unique(return_inverse=True)
      inv = inv[: node_idx.numel()]
  
      node_mask.fill_(False)
      node_mask[subset] = True
      edge_mask = node_mask[row] & node_mask[col]
  
      edge_index = edge_index[:, edge_mask]
  
      if relabel_nodes:
          node_idx = row.new_full((num_nodes,), -1)
          node_idx[subset] = torch.arange(
              subset.size(0), device=row.device
          )
          edge_index = node_idx[edge_index]
  
      return subset, edge_index, inv, edge_mask
  
imports: import os
  import pickle
  import networkx
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from gensim.models import KeyedVectors
  from gensim.scripts.glove2word2vec import glove2word2vec
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.utils.text import VocabDict
  from networkx import convert_node_labels_to_integers
  from torch_geometric.nn import BatchNorm, GCNConv, RGCNConv, SAGEConv
  from tqdm import tqdm
label_desc: Use image node_id 1 for calling a global function with example usage: k_hop_subgraph(node_idx, num_hops, edge_index, relabel_nodes, num_nodes, flow) and returns: subset,  edge_index,  inv,  edge_mask