Fixing up docstring to have defaults

    ----------

    n_tasks: int

      Number of tasks

    n_atom_feat: int, optional

      Number of features per atom.

    n_pair_feat: int, optional

    n_atom_feat: int, optional (default 75)

      Number of features per atom. Note this is 75 by default and should be 78

      if chirality is used by `WeaveFeaturizer`.

    n_pair_feat: int, optional (default 14)

      Number of features per pair of atoms.

    n_hidden: int, optional

    n_hidden: int, optional (default 50)

      Number of units(convolution depths) in corresponding hidden layer

    n_graph_feat: int, optional

    n_graph_feat: int, optional (default 128)

      Number of output features for each molecule(graph)

    n_weave: int, optional

    n_weave: int, optional (default 2)

      The number of weave layers in this model.

    fully_connected_layer_sizes: list

    fully_connected_layer_sizes: list (default `[2000, 100]`)

      The size of each dense layer in the network.  The length of

      this list determines the number of layers.

    conv_weight_init_stddevs: list or float

    conv_weight_init_stddevs: list or float (default 0.03)

      The standard deviation of the distribution to use for weight

      initialization of each convolutional layer. The length of this lisst

      should equal `n_weave`. Alternatively, this may be a single value instead

      of a list, in which case the same value is used for each layer.

    weight_init_stddevs: list or float

    weight_init_stddevs: list or float (default 0.01)

      The standard deviation of the distribution to use for weight

      initialization of each fully connected layer.  The length of this list

      should equal len(layer_sizes).  Alternatively this may be a single value

      instead of a list, in which case the same value is used for every layer.

    bias_init_consts: list or float

    bias_init_consts: list or float (default 0.0)

      The value to initialize the biases in each fully connected layer.  The

      length of this list should equal len(layer_sizes).

      Alternatively this may be a single value instead of a list, in

      which case the same value is used for every layer.

    weight_decay_penalty: float

    weight_decay_penalty: float (default 0.0)

      The magnitude of the weight decay penalty to use

    weight_decay_penalty_type: str

    weight_decay_penalty_type: str (default "l2")

      The type of penalty to use for weight decay, either 'l1' or 'l2'

    dropouts: list or float

    dropouts: list or float (default 0.25)

      The dropout probablity to use for each fully connected layer.  The length of this list

      should equal len(layer_sizes).  Alternatively this may be a single value

      instead of a list, in which case the same value is used for every layer.

    final_conv_activation_fn: Optional[KerasActivationFn]

    final_conv_activation_fn: Optional[KerasActivationFn] (default `tf.nn.tanh`)

      The Tensorflow activation funcntion to apply to the final

      convolution at the end of the weave convolutions. If `None`, then no

      convolution (linear) is applied.

    activation_fns: list or object

      activate is applied (hence linear).

    activation_fns: list or object (default `tf.nn.relu`)

      The Tensorflow activation function to apply to each fully connected layer.  The length

      of this list should equal len(layer_sizes).  Alternatively this may be a

      single value instead of a list, in which case the same value is used for

    compress_post_gaussian_expansion: bool, optional (default False)

      If True, compress the results of the Gaussian expansion back to the

      original dimensions of the input.

    mode: str

    mode: str (default "classification")

      Either "classification" or "regression" for type of model.

    n_classes: int

    n_classes: int (default 2)

      Number of classes to predict (only used in classification mode)

    batch_size: int (default 100)

      Batch size used by this model for training.

    """

    if mode not in ['classification', 'regression']:

      raise ValueError("mode must be either 'classification' or 'regression'")