feat(wandb): add logging through Weights & Biases

    if model.tensorboard:

      for key in scores:

        model._log_value_to_tensorboard(tag=key, simple_value=scores[key])

    if model.wandb:

      wandb.log(scores)

    if self.save_dir is not None:

      score = scores[self.metrics[self.save_metric].name]

      if not self.save_on_minimum:

from deepchem.trans import undo_transforms

from deepchem.utils.evaluate import GeneratorEvaluator

try:

  import wandb

  wandb.ensure_configured()

  if wandb.api.api_key is None:

    _has_wandb = False

    wandb.termwarn("W&B installed but not logged in.  Run `wandb login` or set the WANDB_API_KEY env variable.")

  else:

    _has_wandb = False if os.getenv("WANDB_DISABLED") else True

except (ImportError, AttributeError):

  _has_wandb = False

class KerasModel(Model):

  """This is a DeepChem model implemented by a Keras model.

               learning_rate=0.001,

               optimizer=None,

               tensorboard=False,

               wandb=False,

               log_frequency=100,

               **kwargs):

    """Create a new KerasModel.

      ignored.

    tensorboard: bool

      whether to log progress to TensorBoard during training

    wandb: bool

      whether to log progress to Weights & Biases during training

    log_frequency: int

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

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

    else:

      self.optimizer = optimizer

    self.tensorboard = tensorboard

    # W&B logging

    if wandb and not _has_wandb:

      logger.warning(

        "You set wandb to True but W&B is not installed. To use wandb logging, "

        "run `pip install wandb; wandb login` see https://docs.wandb.com/huggingface."

      )

    self.wandb = wandb and _has_wandb

    # Backwards compatibility

    if "tensorboard_log_frequency" in kwargs:

      logger.warning(

      if self.tensorboard and should_log:

        with self._summary_writer.as_default():

          tf.summary.scalar('loss', batch_loss, current_step)

      if self.wandb and should_log:

        wandb.log({'loss': batch_loss}, step=current_step)

    # Report final results.

    if averaged_batches > 0: