class_docstr: Callback for auto-logging metrics and parameters.
  Records model structural information as params when training begins.
code: def after_fit(self):
      from fastai.callback.all import SaveModelCallback
  
      # Do not log model in case of predicting
      if hasattr(self, "lr_finder") or hasattr(self, "gather_preds"):
          return
  
      # Workaround to log model from SaveModelCallback
      # Use this till able to set order between SaveModelCallback and EarlyStoppingCallback
      for cb in self.cbs:
          if isinstance(cb, SaveModelCallback):
              cb("after_fit")
  
      if self.log_models:
          registered_model_name = get_autologging_config(
              mlflow.fastai.FLAVOR_NAME, "registered_model_name", None
          )
          log_model(
              self.learn,
              artifact_path="model",
              registered_model_name=registered_model_name,
          )
  
file_path: mlflow/mlflow/fastai/callback.py
imports: import logging
  import os
  import tempfile
  from functools import partial
  import matplotlib.pyplot
  import numpy
  from fastai.callback.core import Callback
  import mlflow.tracking
  from mlflow.fastai import log_model
  from mlflow.utils.autologging_utils import ExceptionSafeClass, get_autologging_config
label_desc: Use image node_id 4 for calling the __MlflowFastaiCallback obj's underlying member method code with example usage: obj.after_fit() without return types