class_docstr: Track single values for each metric for each run.
  
  By default, a metric's summary is the last value of its History.
  
  For example, `wandb.log({'accuracy': 0.9})` will add a new step to History and
  update Summary to the latest value. In some cases, it's more useful to have
  the maximum or minimum of a metric instead of the final value. You can set
  history manually `(wandb.summary['accuracy'] = best_acc)`.
  
  In the UI, summary metrics appear in the table to compare across runs.
  Summary metrics are also used in visualizations like the scatter plot and
  parallel coordinates chart.
  
  After training has completed, you may want to save evaluation metrics to a
  run. Summary can handle numpy arrays and PyTorch/TensorFlow tensors. When
  you save one of these types to Summary, we persist the entire tensor in a
  binary file and store high level metrics in the summary object, such as min,
  mean, variance, and 95th percentile.
  
  Examples:
      ```python
      wandb.init(config=args)
  
      best_accuracy = 0
      for epoch in range(1, args.epochs + 1):
          test_loss, test_accuracy = test()
          if test_accuracy > best_accuracy:
              wandb.run.summary["best_accuracy"] = test_accuracy
              best_accuracy = test_accuracy
      ```
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code: def __init__(self, get_current_summary_callback: t.Callable):
      super().__init__()
      object.__setattr__(self, "_update_callback", None)
      object.__setattr__(
          self,
          "_get_current_summary_callback",
          get_current_summary_callback,
      )
  
imports: import abc
  import typing
  from .interface.summary_record import SummaryItem, SummaryRecord
label_desc: Use image node_id 1 to create a new Summary object from inherited base classes: SummaryDict with example: obj = Summary(get_current_summary_callback)