Refactor code in contrib/tensorflow_models/

import numpy as np

import tensorflow as tf

try:

  from collections.abc import Sequence as SequenceCollection

except:

  from collections import Sequence as SequenceCollection

from deepchem.nn import model_ops

class RobustMultitaskClassifier(MultiTaskClassifier):

    n_layers = len(layer_sizes)

    assert n_layers == len(bypass_layer_sizes)

    if not isinstance(weight_init_stddevs, collections.Sequence):

    if not isinstance(weight_init_stddevs, SequenceCollection):

      weight_init_stddevs = [weight_init_stddevs] * n_layers

    if not isinstance(bypass_weight_init_stddevs, collections.Sequence):

    if not isinstance(bypass_weight_init_stddevs, SequenceCollection):

      bypass_weight_init_stddevs = [bypass_weight_init_stddevs] * n_layers

    if not isinstance(bias_init_consts, collections.Sequence):

    if not isinstance(bias_init_consts, SequenceCollection):

      bias_init_consts = [bias_init_consts] * n_layers

    if not isinstance(dropouts, collections.Sequence):

    if not isinstance(dropouts, SequenceCollection):

      dropouts = [dropouts] * n_layers

    if not isinstance(activation_fns, collections.Sequence):

    if not isinstance(activation_fns, SequenceCollection):

      activation_fns = [activation_fns] * n_layers

    # Add the input features.