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 _log(self, metric, value):
      if self.azurerun is not None:
          self.azurerun.log(metric, value)
          if self.log_to_parent and self.azurerun.parent is not None:
              self.azurerun.parent.log(metric, value)
  
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 5 for calling the AzureMLCallback obj's underlying member method code with example usage: obj._log(metric, value) without return types