class_docstr: Implements a progressive multitask neural network for regression.
  
  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.
  
  References
  ----------
  See [1]_ for a full description of the progressive architecture
  
  .. [1] Rusu, Andrei A., et al. "Progressive neural networks." arXiv preprint
      arXiv:1606.04671 (2016).
code: def create_output(self, layer):
      return layer
  
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 3 for calling the ProgressiveMultitaskRegressor obj's underlying member method code with example usage: obj.create_output(layer) and returns: layer