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 __init__(
      self,
      n_tasks,
      n_features,
      alpha_init_stddevs=0.02,
      layer_sizes=[1000],
      weight_init_stddevs=0.02,
      bias_init_consts=1.0,
      weight_decay_penalty=0.0,
      weight_decay_penalty_type="l2",
      dropouts=0.5,
      activation_fns=tf.nn.relu,
      **kwargs,
  ):
      n_outputs = 2
      super(ProgressiveMultitaskClassifier, self).__init__(
          n_tasks,
          n_features,
          alpha_init_stddevs=alpha_init_stddevs,
          layer_sizes=layer_sizes,
          weight_init_stddevs=weight_init_stddevs,
          bias_init_consts=bias_init_consts,
          weight_decay_penalty=weight_decay_penalty,
          weight_decay_penalty_type=weight_decay_penalty_type,
          dropouts=dropouts,
          activation_fns=activation_fns,
          n_outputs=n_outputs,
          **kwargs,
      )
  
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 1 to create a new ProgressiveMultitaskClassifier object from inherited base classes: ProgressiveMultitaskRegressor with example: obj = ProgressiveMultitaskClassifier(n_tasks, n_features, alpha_init_stddevs, layer_sizes, weight_init_stddevs, bias_init_consts, weight_decay_penalty, weight_decay_penalty_type, dropouts, activation_fns)