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.
class_docstr_tok: ["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","."]
code: def __init__(self, pool, head, task, num_tasks, num_classes):
      super().__init__()
      self.pool = pool
      self.head = head
      self.task = task
      self.num_tasks = num_tasks
      self.num_classes = num_classes
  
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 to create a new GNNHead object from inherited base classes: torch.nn with example: obj = GNNHead(pool, head, task, num_tasks, num_classes)