code: def __call__(self, outputs, labels, weights):
      outputs = [t[:, self.task] for t in outputs]
      labels = [t[:, self.task] for t in labels]
      weights = [t[:, self.task] for t in weights]
      return super(TaskLoss, self).__call__(outputs, labels, weights)
  
imports: import numpy
  import tensorflow
  from collections.abc import Sequence
  import logging
  from deepchem.models import KerasModel, layers
  from deepchem.models.losses import L2Loss, SparseSoftmaxCrossEntropy
  from deepchem.models.keras_model import _StandardLoss
  from tensorflow.keras.layers import Input, Dense, Dropout, ReLU, Concatenate, Add, Multiply, Softmax
label_desc: Use image node_id 2 for calling the TaskLoss obj's underlying member method code with example usage: obj.__call__(outputs, labels, weights) and returns: super