class_docstr: Implements a progressive multitask neural network for classification.
  
  Progressive Networks: https://arxiv.org/pdf/1606.04671v3.pdf
  
  Progressive networks allow for multitask learning where each task
  gets a new column of weights. As a result, there is no exponential
  forgetting where previous tasks are ignored.
code: def create_loss(self):
      return SparseSoftmaxCrossEntropy()
  
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 ProgressiveMultitaskClassifier obj's underlying member method code with example usage: obj.create_loss() and returns: SparseSoftmaxCrossEntropy