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 after_epoch(self):
      # log metrics
      for n, v in zip(
          self.learn.recorder.metric_names, self.learn.recorder.log
      ):
          if n not in ["epoch", "time"]:
              self._log(f"epoch__{n}", v)
          if n == "time":
              # split elapsed time string, then convert into 'seconds' to log
              m, s = str(v).split(":")
              elapsed = int(m) * 60 + int(s)
              self._log(f"epoch__{n}", elapsed)
  
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 4 for calling the AzureMLCallback obj's underlying member method code with example usage: obj.after_epoch() without return types