docstr: Weights and biases (https://www.wandb.ai/) is a tool for experiment tracking,
  model optimization, and dataset versioning.
  
  WandbLoggerCallback automatically uses Weights and biases to log metric results at the end
  of each epoch. User could choose whether to watch model gradients and whether to log
  hyper-parameters defined in config.
  
  Example:
      >>> wnb_callback = WandbLoggerCallback(
      ...     project='my_project',
      ... )
  :param project: Name of the Wandb project. Mandatory.
  :param log_config: Whether to log ``config``.
  :param watch_model: Whether to log model gradients and model topology.
  :param **kwargs: The keyword arguments will be pased to ``wandb.init()``.
code: def __init__(
      self,
      project: str,
      log_config: bool = False,
      watch_model: bool = False,
      **kwargs,
  ):
      """
      Weights and biases (https://www.wandb.ai/) is a tool for experiment tracking,
      model optimization, and dataset versioning.
  
      WandbLoggerCallback automatically uses Weights and biases to log metric results at the end
      of each epoch. User could choose whether to watch model gradients and whether to log
      hyper-parameters defined in config.
  
      Example:
          >>> wnb_callback = WandbLoggerCallback(
          ...     project='my_project',
          ... )
      :param project: Name of the Wandb project. Mandatory.
      :param log_config: Whether to log ``config``.
      :param watch_model: Whether to log model gradients and model topology.
      :param **kwargs: The keyword arguments will be pased to ``wandb.init()``.
      """
      try:
          wandb.ensure_configured()
      except AttributeError:
          invalidInputError(
              False,
              "You should run `wandb login` from the command line first.",
          )
      self.project = project
      self.log_config = log_config
      self.watch_model = watch_model
      self.kwargs = kwargs
      super().__init__()
  
file_path: BigDL/python/orca/src/bigdl/orca/learn/pytorch/callbacks/wandb.py
imports: from bigdl.orca.learn.pytorch.callbacks import Callback
  from bigdl.dllib.utils.log4Error import invalidInputError
  import copy
label_desc: Use image node_id 1 to create a new WandbLoggerCallback object from inherited base classes: Callback with example: obj = WandbLoggerCallback(project, log_config, watch_model)