code: def negative_edge_sampler(input_graph: BatchGraphData):
      """
      NegativeEdge is a function that adds negative edges to the input graph data. It randomly samples negative edges (edges that do not exist in the original graph) and adds them to the input graph data.
      The number of negative edges added is equal to half the number of edges in the original graph. This is useful for tasks like edge prediction, where the model needs to learn to differentiate between existing and non-existing edges.
  
      Parameters
      ----------
      input_graph: dc.feat.graph_data.BatchGraphData
          The input graph data.
  
      Returns
      -------
      BatchGraphData
          A new BatchGraphData object with the additional attribute `negative_edge_index`.
  
      Example
      -------
      >>> import numpy as np
      >>> import torch
      >>> from deepchem.feat.graph_data import BatchGraphData, GraphData
      >>> from deepchem.models.torch_models.gnn import negative_edge_sampler
      >>> num_nodes_list, num_edge_list = [3, 4, 5], [2, 4, 5]
      >>> num_node_features, num_edge_features = 32, 32
      >>> edge_index_list = [
      ...     np.array([[0, 1], [1, 2]]),
      ...     np.array([[0, 1, 2, 3], [1, 2, 0, 2]]),
      ...     np.array([[0, 1, 2, 3, 4], [1, 2, 3, 4, 0]]),
      ... ]
      >>> graph_list = [
      ...     GraphData(node_features=np.random.random_sample(
      ...         (num_nodes_list[i], num_node_features)),
      ...               edge_index=edge_index_list[i],
      ...               edge_features=np.random.random_sample(
      ...                   (num_edge_list[i], num_edge_features)),
      ...               node_pos_features=None) for i in range(len(num_edge_list))
      ... ]
      >>> batched_graph = BatchGraphData(graph_list)
      >>> batched_graph = batched_graph.numpy_to_torch()
      >>> neg_sampled = negative_edge_sampler(batched_graph)
      """
      data = copy.deepcopy(input_graph)
  
      num_nodes = data.num_nodes
      num_edges = data.num_edges
  
      edge_set = set(
          [
              str(data.edge_index[0, i].cpu().item())
              + ","
              + str(data.edge_index[1, i].cpu().item())
              for i in range(data.edge_index.shape[1])
          ]
      )
  
      redandunt_sample = torch.randint(0, num_nodes, (2, 5 * num_edges))
      sampled_ind = []
      sampled_edge_set = set([])
      for i in range(5 * num_edges):
          node1 = redandunt_sample[0, i].cpu().item()
          node2 = redandunt_sample[1, i].cpu().item()
          edge_str = str(node1) + "," + str(node2)
          if (
              edge_str not in edge_set
              and edge_str not in sampled_edge_set
              and not node1 == node2
          ):
              sampled_edge_set.add(edge_str)
              sampled_ind.append(i)
          if len(sampled_ind) == num_edges / 2:
              break
  
      data.negative_edge_index = redandunt_sample[:, sampled_ind]  # type: ignore
  
      return data
  
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 1 for calling a global function with example usage: negative_edge_sampler(input_graph) and returns: data