class_docstr: Log losses, metrics, model architecture summary to AzureML.
  
  If `log_offline` is False, will only log if actually running on AzureML.
  A custom AzureML `Run` class can be passed as `azurerun`.
  If `log_to_parent` is True, will also log to the parent run, if exists (e.g. in AzureML pipelines).
class_docstr_tok: ["Log","losses",",","metrics",",","model","architecture","summary","to","AzureML",".","If","`","log_offline","`","is","False",",","will","only","log","if","actually","running","on","AzureML",".","A","custom","AzureML","`","Run","`","class","can","be","passed","as","`","azurerun","`",".","If","`","log_to_parent","`","is","True",",","will","also","log","to","the","parent","run",",","if","exists","(","e.g",".","in","AzureML","pipelines",")","."]
code: def before_fit(self):
      self._log("n_epoch", self.learn.n_epoch)
      self._log("model_class", str(type(self.learn.model)))
  
      try:
          summary_file = Path("outputs") / "model_summary.txt"
          with summary_file.open("w") as f:
              f.write(repr(self.learn.model))
      except:
          print(
              "Did not log model summary. Check if your model is PyTorch model."
          )
  
file_path: fastai/fastai/callback/azureml.py
imports: from ..basics import *
  from ..learner import Callback
  from azureml.core.run import Run
  from azureml.exceptions import RunEnvironmentException
  import warnings
label_desc: Use image node_id 2 for calling the AzureMLCallback obj's underlying member method code with example usage: obj.before_fit() without return types