Version issues with Savemodel

[jrossthomson]

tfx/docs/tutorials/model_analysis/tfma_basic.ipynb

Line 320 in f6058c1

"### Keras"

Get's the follow set of warnings. Needs to be updated.

WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory.
WARNING:absl:Tensorflow version (2.15.1) found. Note that TFMA support for TF 2.0 is currently in beta
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
/usr/local/lib/python3.10/dist-packages/tensorflow_model_analysis/metrics/binary_confusion_matrices.py:152: RuntimeWarning: invalid value encountered in divide
  f1 = 2 * precision * recall / (precision + recall)
/usr/local/lib/python3.10/dist-packages/tensorflow_model_analysis/metrics/binary_confusion_matrices.py:155: RuntimeWarning: invalid value encountered in divide
  false_omission_rate = fn / predicated_negatives
WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:112: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`