minor updates to code: comments, guardrails, warnings

      for key in scores:

        model._log_scalar_to_tensorboard(key, scores[key],

                                         model.get_global_step())

    if model.wandb:

      import wandb

      wandb.log(scores, step=step)

    if self.save_dir is not None:

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

      if not self.save_on_minimum:

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

        model.save_checkpoint(model_dir=self.save_dir)

        self._best_score = score

    if model.wandb_logger is not None:

    if model.wandb or (model.wandb_logger is not None):

      # Log data to Wandb

      data = {'eval/' + k: v for k, v in scores.items()}

      model.wandb_logger.log_data(data, step)

import deepchem as dc

import numpy as np

import tensorflow as tf

import unittest

try:

  import wandb

  has_wandb = True

except:

  has_wandb = False

def test_overfit_graph_model():

  assert file_size > 0

@unittest.skipIf(not has_wandb, 'Wandb is not installed')

def test_wandblogger():

  """Test logging to Weights & Biases."""

  # Load dataset and Models

    regularization_loss: Callable, optional

      a function that takes no arguments, and returns an extra contribution to add

      to the loss function

    wandb_logger: WandbLogger

      the Weights & Biases logger object used to log data and metrics

    """

    super(TorchModel, self).__init__(model=model, model_dir=model_dir, **kwargs)

    if isinstance(loss, Loss):

    self.model = model.to(device)

    # W&B logging

    if wandb:

      logger.warning(

          "`wandb` argument is deprecated. Please use `wandb_logger` instead. "

          "This argument will be removed in a future release of DeepChem.")

    if wandb and not _has_wandb:

      logger.warning(

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

        learning_rate=learning_rate,

        optimizer=optimizer,

        tensorboard=tensorboard,

        log_frequency=log_frequency)

        log_frequency=log_frequency,

        regularization_loss=regularization_loss)

    wandb_logger_config.update(**kwargs)

    if self.wandb_logger is not None: