docstr: Constructs the computational graph.
  
  This function constructs the computational graph for the model. It relies
  subclassed methods (build/cost) to construct specific graphs.
  
  Parameters
  ----------
  n_tasks: int
    Number of tasks
  n_features: int
    Number of features.
  n_classes: int
    Number of classes if this is for classification.
code: def __init__(self, n_tasks, n_features, n_classes=2, **kwargs):
      """Constructs the computational graph.
  
      This function constructs the computational graph for the model. It relies
      subclassed methods (build/cost) to construct specific graphs.
  
      Parameters
      ----------
      n_tasks: int
        Number of tasks
      n_features: int
        Number of features.
      n_classes: int
        Number of classes if this is for classification.
      """
      # Save hyperparameters
      self.n_tasks = n_tasks
      self.n_features = n_features
      self.n_classes = n_classes
      super(TorchMultitaskClassification, self).__init__(**kwargs)
  
file_path: deepchem/contrib/torch/torch_multitask_classification.py
imports: import torch
  import numpy
  from deepchem.metrics import from_one_hot
  from torch_model import TorchMultitaskModel
label_desc: Use image node_id 1 to create a new TorchMultitaskClassification object from inherited base classes: TorchMultitaskModel with example: obj = TorchMultitaskClassification(n_tasks, n_features, n_classes)