code: def _model(dense_size, learning_rate):
      # from https://www.tensorflow.org/guide/keras/custom_callback
      model = keras.Sequential()
      model.add(keras.layers.Dense(1, input_dim=dense_size))
      model.compile(
          optimizer=keras.optimizers.RMSprop(
              learning_rate=learning_rate
          ),
          loss="mean_squared_error",
          metrics=["mean_absolute_error"],
      )
  
      # Load example MNIST data and pre-process it
      (x_train, y_train), (
          x_test,
          y_test,
      ) = tf.keras.datasets.mnist.load_data()
      x_train = x_train.reshape(-1, 784).astype("float32") / 255.0
      x_test = x_test.reshape(-1, 784).astype("float32") / 255.0
  
      # Limit the data to 1000 samples
      x_train = x_train[:1000]
      y_train = y_train[:1000]
      x_test = x_test[:1000]
      y_test = y_test[:1000]
      return model, x_train, y_train
  
file_path: keepsake/python/tests/test_keras_callback.py
imports: import json
  from glob import glob
  import tensorflow
  from tensorflow import keras
  import pytest
  import os
  from keepsake.keras_callback import KeepsakeCallback
label_desc: Use image node_id 1 for calling a global function with example usage: _model(dense_size, learning_rate) and returns: model,  x_train,  y_train