class_docstr: Callback for auto-logging metrics and parameters.
  Records model structural information as params when training begins.
code: def __init__(self, metrics_logger, log_models, is_fine_tune=False):
      super().__init__()
      self.metrics_logger = metrics_logger
      self.log_models = log_models
      self.is_fine_tune = is_fine_tune
      self.freeze_prefix = ""
  
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 1 to create a new __MlflowFastaiCallback object from inherited base classes: Callback with example: obj = __MlflowFastaiCallback(metrics_logger, log_models, is_fine_tune)