📝 update docstrings

import logging

import numpy as np

import multiprocessing

from typing import Any, Dict, List, Iterable, Sequence, Tuple

from typing import Any, Dict, List, Iterable, Sequence, Tuple, Union

from deepchem.utils.typing import PymatgenStructure

logger = logging.getLogger(__name__)

  """

  def featurize(self,

                structures: Iterable[Dict[str, Any]],

                structures: Iterable[Union[Dict[str, Any], PymatgenStructure]],

                log_every_n: int = 1000) -> np.ndarray:

    """Calculate features for crystal structures.

    Parameters

    ----------

    structures: Iterable[Union[Dict, pymatgen.Structure]

    structures: Iterable[Union[Dict, pymatgen.Structure]]

      Iterable sequence of pymatgen structure dictionaries

      or pymatgen.Structure. Please confirm the dictionary representations

      of pymatgen.Structure from https://pymatgen.org/pymatgen.core.structure.html.

class CGCNNModel(TorchModel):

  """CGCNN wrapper model for converting PyTorch style to Keras style.

  """Crystal Graph Convolutional Neural Network (CGCNN).

  Please confirm the details about CGCNN from CGCNN class docstring.

  Here is a simple example of code that uses the CGCNNModel with

  materials dataset.

               n_tasks: int = 1,

               **kwargs):

    """

    This class accepts all the keyword arguments from TorchModel.

    Parameters

    ----------

    in_node_dim: int, default 92

      Size for hidden representations in the output MLP predictor, default to 128.

    n_tasks: int, default 1

      Number of the output size, default to 1.

    loss: dc.models.losses.Loss or function

      A Loss or function defining how to compute the training loss for each

      batch, please confirm the details from `TorchModel` docstring.

    output_types: list of strings, optional (default None)

      the type of each output from the model, as described above

    batch_size: int, optional (default 100)

      default batch size for training and evaluating

    model_dir: str, optional (default None)

      the directory on disk where the model will be stored.  If this is None,

      a temporary directory is created.

    learning_rate: float or LearningRateSchedule, optional (default 0.001)

      the learning rate to use for fitting.  If optimizer is specified, this is

      ignored.

    optimizer: Optimizer, optional (default None)

      the optimizer to use for fitting.  If this is specified, learning_rate is

      ignored.

    tensorboard: bool, optional (default False)

      whether to log progress to TensorBoard during training

    wandb: bool, optional (default False)

      whether to log progress to Weights & Biases during training

    log_frequency: int, optional (default 100)

      The frequency at which to log data. Data is logged using

      `logging` by default. If `tensorboard` is set, data is also

      logged to TensorBoard. If `wandb` is set, data is also logged

      to Weights & Biases. Logging happens at global steps. Roughly,

      a global step corresponds to one batch of training. If you'd

      like a printout every 10 batch steps, you'd set

      `log_frequency=10` for example.

    device: torch.device, optional (default None)

      the device on which to run computations.  If None, a device is

      chosen automatically.

    kwargs: Dict

      This class accepts all the keyword arguments from TorchModel.

    """

    model = CGCNN(in_node_dim, hidden_node_dim, in_edge_dim, num_conv,

                  predicator_hidden_feats, n_tasks)