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.
docstr_tok: ["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, **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
      super(TorchMultitaskRegression, self).__init__(**kwargs)
  
file_path: deepchem/contrib/torch/torch_multitask_regression.py
imports: import torch
  import numpy
  from torch_model import TorchMultitaskModel
label_desc: Use image node_id 1 to create a new TorchMultitaskRegression object from inherited base classes: TorchMultitaskModel with example: obj = TorchMultitaskRegression(n_tasks, n_features)