class_docstr: Callback for auto-logging metrics and parameters.
  Records model structural information as params when training begins.
docstr: Log loss and other metrics values after each epoch
docstr_tok: ["Log","loss","and","other","metrics","values","after","each","epoch"]
code: def after_epoch(self):
      """Log loss and other metrics values after each epoch"""
  
      def _is_float(x):
          try:
              float(x)
              return True
          except (ValueError, TypeError):
              return False
  
      # Do not record in case of predicting
      if hasattr(self, "lr_finder") or hasattr(self, "gather_preds"):
          return
      # Remove non-float metrics and record the rest.
      metrics = self.recorder.log
      metrics = {
          k: v
          for k, v in zip(self.recorder.metric_names, metrics)
          if _is_float(v)
      }
      self.metrics_logger.record_metrics(metrics, step=metrics["epoch"])
  
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 2 for calling the __MlflowFastaiCallback obj's underlying member method code with example usage: obj.after_epoch() and returns: True, False