class_docstr: Abstract base class used to build new callbacks.
  
  The `logs` dictionary that callback methods
  take as argument will contain keys for quantities relevant to
  the current batch or epoch.
  
  Currently, the `.fit()` method of the `Model` class
  will include the following quantities in the `logs` that
  it passes to its callbacks:
  
  ```python
  on_epoch_end: logs include `acc` and `loss`, and
      optionally include `val_loss`
      (if validation is enabled in `fit`), and `val_acc`
      (if validation and accuracy monitoring are enabled).
  on_train_batch_begin: logs include `size`,
      the number of samples in the current batch.
  on_train_batch_end: logs include `loss`, and optionally `acc`
      (if accuracy monitoring is enabled).
  ```
  
  Attributes:
      params (dict):  Training parameters
          (eg. verbosity, batch size, number of epochs...).
      model (elegy.model.Model): Reference of the model being trained.
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docstr: Called at the beginning of a training batch in `fit` methods.
  
  Subclasses should override for any actions to run.
  
  Arguments:
      batch: integer, index of batch within the current epoch.
      logs: dict. Has keys `batch` and `size` representing the current batch
          number and the size of the batch.
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code: def on_train_batch_begin(
      self,
      batch: int,
      logs: tp.Optional[tp.Dict[str, np.ndarray]] = None,
  ):
      """Called at the beginning of a training batch in `fit` methods.
  
      Subclasses should override for any actions to run.
  
      Arguments:
          batch: integer, index of batch within the current epoch.
          logs: dict. Has keys `batch` and `size` representing the current batch
              number and the size of the batch.
      """
      pass
  
imports: import typing
  import numpy
label_desc: Use image node_id 6 for calling the Callback obj's underlying member method code with example usage: obj.on_train_batch_begin(batch, logs) without return types