created wandb branch

      if self._best_score is None or score < self._best_score:

        model.save_checkpoint(model_dir=self.save_dir)

        self._best_score = score

class WandbCallback(object):

  """

  Weights & Biases Logger

  """

  def __init__(self,

              **kwargs):

    try:

      import wandb

    except ImportError:

      raise ImportError(

        'You want to use `wandb` logger which is not installed yet,'

        ' install it with `pip install wandb`.'

      )

  def __call__(self, model, step):

    """This is invoked by the KerasModel after every step of fitting.

    Parameters

    ----------

    model: KerasModel

      the model that is being trained

    step: int

      the index of the training step that has just completed

    """