class_docstr: Prediction head module for the GNNModular model.
  
  Parameters
  ----------
  pool: Union[function,torch.nn.Module]
      Pooling function or nn.Module to use
  head: torch.nn.Module
      Prediction head to use
  task: str
      The type of task. Must be one of "regression", "classification".
  num_tasks: int
      Number of tasks.
  num_classes: int
      Number of classes for classification.
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docstr: Forward pass for the GNN head module.
  
  Parameters
  ----------
  data: tuple
      A tuple containing the node representations and the input graph data.
      node_representation is a torch.Tensor created after passing input through the GNN layers.
      input_batch is the original input BatchGraphData.
docstr_tok: ["Forward","pass","for","the","GNN","head","module",".","Parameters","--","--","--","--","--","data",":","tuple","A","tuple","containing","the","node","representations","and","the","input","graph","data",".","node_representation","is","a","torch.Tensor","created","after","passing","input","through","the","GNN","layers",".","input_batch","is","the","original","input","BatchGraphData","."]
code: def forward(self, data):
      """
      Forward pass for the GNN head module.
  
      Parameters
      ----------
      data: tuple
          A tuple containing the node representations and the input graph data.
          node_representation is a torch.Tensor created after passing input through the GNN layers.
          input_batch is the original input BatchGraphData.
      """
      node_representation, input_batch = data
  
      pooled = self.pool(node_representation, input_batch.graph_index)
      out = self.head(pooled)
      if self.task == "classification":
          out = torch.reshape(
              out, (-1, self.num_tasks, self.num_classes)
          )
      return out
  
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 2 for calling the GNNHead obj's underlying member method code with example usage: obj.forward(data) and returns: out