A workaround is to temporarily change the global policy to float32 when loading, then change it back to  mixed precision. For example:
    tf.keras.mixed_precision.set_global_policy('float32')  # change policy

   *TRANSFER_MODEL = tf.keras.Sequential([
   *     hub.KerasLayer("https://tfhub.dev/tensorflow/efficientnet/b0/classification/1")
   *    ])

   *tf.keras.mixed_precision.set_global_policy('mixed_bfloat16')  # change policy back