class_docstr: SubgraphX from `On Explainability of Graph Neural Networks via Subgraph
  Explorations <https://arxiv.org/abs/2102.05152>`__, adapted for heterogeneous graphs
  
  It identifies the most important subgraph from the original graph that
  plays a critical role in GNN-based graph classification.
  
  It employs Monte Carlo tree search (MCTS) in efficiently exploring
  different subgraphs for explanation and uses Shapley values as the measure
  of subgraph importance.
  
  Parameters
  ----------
  model : nn.Module
      The GNN model to explain that tackles multiclass graph classification
  
      * Its forward function must have the form
        :attr:`forward(self, graph, nfeat)`.
      * The output of its forward function is the logits.
  num_hops : int
      Number of message passing layers in the model
  coef : float, optional
      This hyperparameter controls the trade-off between exploration and
      exploitation. A higher value encourages the algorithm to explore
      relatively unvisited nodes. Default: 10.0
  high2low : bool, optional
      If True, it will use the "High2low" strategy for pruning actions,
      expanding children nodes from high degree to low degree when extending
      the children nodes in the search tree. Otherwise, it will use the
      "Low2high" strategy. Default: True
  num_child : int, optional
      This is the number of children nodes to expand when extending the
      children nodes in the search tree. Default: 12
  num_rollouts : int, optional
      This is the number of rollouts for MCTS. Default: 20
  node_min : int, optional
      This is the threshold to define a leaf node based on the number of
      nodes in a subgraph. Default: 3
  shapley_steps : int, optional
      This is the number of steps for Monte Carlo sampling in estimating
      Shapley values. Default: 100
  log : bool, optional
      If True, it will log the progress. Default: False
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code: def __init__(
      self,
      model,
      num_hops,
      coef=10.0,
      high2low=True,
      num_child=12,
      num_rollouts=20,
      node_min=3,
      shapley_steps=100,
      log=False,
  ):
      super().__init__()
      self.num_hops = num_hops
      self.coef = coef
      self.high2low = high2low
      self.num_child = num_child
      self.num_rollouts = num_rollouts
      self.node_min = node_min
      self.shapley_steps = shapley_steps
      self.log = log
  
      self.model = model
  
imports: import math
  import networkx
  import numpy
  import torch
  import torch.nn
  from ....None import to_heterogeneous, to_homogeneous
  from ....base import NID
  from ....convert import to_networkx
  from ....subgraph import node_subgraph
  from ....transforms.functional import remove_nodes
label_desc: Use image node_id 1 to create a new HeteroSubgraphX object from inherited base classes: nn with example: obj = HeteroSubgraphX(model, num_hops, coef, high2low, num_child, num_rollouts, node_min, shapley_steps, log)