code: def context_pred_sampler(
      input_graph: GraphData, l1: int, l2: int, root_idx=None
  ):
      """
      Generate subgraph and context graph for context prediction.
  
      This function takes an input graph and generates a subgraph and a context graph for context prediction. The subgraph is the entire input graph, while the context graph is a ring around the root node outside of l1 and inside of l2.
  
      Parameters
      ----------
      input_graph : GraphData
          The input graph for which the subgraph and context graph are to be generated.
      l1 : int
          The inner diameter of the context graph.
      l2 : int
          The outer diameter of the context graph.
      root_idx : int, optional
          The index of the root node. If not provided, a random node will be selected as the root.
  
      Returns
      -------
      subgraph : GraphData
          The subgraph generated from the input graph.
      s_overlap : list
          The indices of overlapping nodes between substruct and context, with respect to the substruct ordering.
      context_G : GraphData
          The context graph generated from the input graph.
      c_overlap : list
          The indices of overlapping nodes between substruct and context, with respect to the context ordering.
  
      Examples
      --------
      >>> import numpy as np
      >>> from deepchem.feat.graph_data import GraphData
      >>> from deepchem.models.torch_models.gnn import context_pred_sampler
      >>> x = np.array([[1, 0], [0, 1], [1, 1], [0, 0], [1, 0], [0, 1], [1, 1], [0, 0]])
      >>> edge_index = np.array([[0, 1, 2, 3, 4, 5, 6, 7], [1, 0, 3, 2, 5, 4, 7, 6]])
      >>> edge_feats = np.array([[1, 0], [0, 1], [1, 1], [0, 0], [1, 0], [0, 1], [1, 1], [0, 0]])
      >>> graph_index = np.array([0, 0, 1, 1, 2, 2, 3, 3])
      >>> data = GraphData(node_features=x,
      ...                  edge_index=edge_index,
      ...                  edge_features=edge_feats,
      ...                  graph_index=graph_index)
      >>> l1 = 1
      >>> l2 = 2
      >>> subgraph, s_overlap, context_G, c_overlap = context_pred_sampler(data, l1, l2)
      """
      data = copy.deepcopy(input_graph)
  
      root_idx = random.sample(range(data.num_nodes), 1)[0]
  
      # Take the entire graph, but can be modified to take a subgraph of k-hops from the root node
      x_substruct = data.node_features
      edge_attr_substruct = data.edge_features
      edge_index_substruct = data.edge_index
  
      subgraph = GraphData(
          node_features=x_substruct,
          edge_features=edge_attr_substruct,
          edge_index=edge_index_substruct,
      )
  
      # Get node idx between root and the inner diameter l1
      l1_node_idxes = shortest_path_length(data, root_idx, l1).keys()
      # Get node idx between root and the outer diameter l2
      l2_node_idxes = shortest_path_length(data, root_idx, l2).keys()
  
      # takes a ring around the root node outside of l1 and inside of l2
      context_node_idxes = set(l1_node_idxes).symmetric_difference(
          set(l2_node_idxes)
      )
  
      if len(context_node_idxes) > 0:
          context_G, context_node_map = data.subgraph(
              context_node_idxes
          )
          # Get indices of overlapping nodes between substruct and context, WRT context ordering
          s_overlap = list(context_node_idxes)
          c_overlap = [
              context_node_map[old_idx] for old_idx in s_overlap
          ]
          return (subgraph, s_overlap, context_G, c_overlap)
      else:
          return (subgraph, [], None, [])
  
imports: import random
  import copy
  import torch
  import numpy
  from torch_geometric.nn import GINEConv, GCNConv, GATConv, SAGEConv, global_add_pool, global_mean_pool, global_max_pool
  from torch_geometric.nn.aggr import AttentionalAggregation, Set2Set
  from torch_geometric.nn.inits import uniform
  import torch.nn
  from torch.functional import F
  from deepchem.data import Dataset
  from deepchem.models.losses import SoftmaxCrossEntropy, EdgePredictionLoss, GraphNodeMaskingLoss, GraphEdgeMaskingLoss, DeepGraphInfomaxLoss, GraphContextPredLoss
  from deepchem.models.torch_models import ModularTorchModel
  from deepchem.feat.graph_data import BatchGraphData, GraphData, shortest_path_length
  from typing import Iterable, List, Tuple
  from deepchem.metrics import to_one_hot
label_desc: Use image node_id 5 for calling a global function with example usage: context_pred_sampler(input_graph, l1, l2, root_idx) and returns: subgraph,  s_overlap,  context_G,  c_overlap, subgraph,  list,  None,  list