class_docstr: Callback for auto-logging parameters and metrics in TensorFlow >= 2.0.0.
  Records model structural information as params when training starts.
class_docstr_tok: ["Callback","for","auto-logging","parameters","and","metrics","in","TensorFlow",">","=","2.0.0",".","Records","model","structural","information","as","params","when","training","starts","."]
code: def on_epoch_end(self, epoch, logs=None):
      # NB: tf.Keras uses zero-indexing for epochs, while other TensorFlow Estimator
      # APIs (e.g., tf.Estimator) use one-indexing. Accordingly, the modular arithmetic
      # used here is slightly different from the arithmetic used in `_log_event`, which
      # provides  metric logging hooks for TensorFlow Estimator & other TensorFlow APIs
      if epoch % self.log_every_n_steps == 0:
          self.metrics_logger.record_metrics(logs, epoch)
  
imports: import warnings
  from typing import Dict, Union
  import numpy
  import tensorflow
  from tensorflow.keras.callbacks import Callback, TensorBoard
  import mlflow
  from mlflow.utils.autologging_utils import INPUT_EXAMPLE_SAMPLE_ROWS, ExceptionSafeClass
label_desc: Use image node_id 5 for calling the __MLflowTfKeras2Callback obj's underlying member method code with example usage: obj.on_epoch_end(epoch, logs) without return types