[[autodoc]] integrations.CometCallback
    - setup
[[autodoc]] DefaultFlowCallback
[[autodoc]] PrinterCallback
[[autodoc]] ProgressCallback
[[autodoc]] EarlyStoppingCallback
[[autodoc]] integrations.TensorBoardCallback
[[autodoc]] integrations.WandbCallback
    - setup
[[autodoc]] integrations.MLflowCallback
    - setup
[[autodoc]] integrations.AzureMLCallback
TrainerCallback
[[autodoc]] TrainerCallback
Here is an example of how to register a custom callback with the PyTorch [Trainer]:
```python
class MyCallback(TrainerCallback):
    "A callback that prints a message at the beginning of training"
def on_train_begin(self, args, state, control, **kwargs):
    print("Starting training")
trainer = Trainer(
    model,
    args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    callbacks=[MyCallback],  # We can either pass the callback class this way or an instance of it (MyCallback())
