How to Load Data with Python Generators for Multi-Modal and Multi-Task

[daraghimo]

I would like to thank the authors and contributors of this wonderful AutoML system.
I am not a master of tensorflow and I depend on your tutorial and documentation.
The tutorial here show the way to load x,y data with python generators to a StructuredDataClassifier. What about a Customized Search Space case like the following example in the tutorial here

input_node1 = ak.ImageInput()
output_node = ak.Normalization()(input_node1)
output_node = ak.ImageAugmentation()(output_node)
output_node1 = ak.ConvBlock()(output_node)
output_node2 = ak.ResNetBlock(version="v2")(output_node)
output_node1 = ak.Merge()([output_node1, output_node2])

input_node2 = ak.StructuredDataInput()
output_node = ak.CategoricalToNumerical()(input_node2)
output_node2 = ak.DenseBlock()(output_node)

output_node = ak.Merge()([output_node1, output_node2])
output_node1 = ak.ClassificationHead()(output_node)
output_node2 = ak.RegressionHead()(output_node)

auto_model = ak.AutoModel(
    inputs=[input_node1, input_node2],
    outputs=[output_node1, output_node2],
    overwrite=True,
    max_trials=2,
)

image_data = np.random.rand(num_instances, 32, 32, 3).astype(np.float32)
structured_data = np.random.rand(num_instances, 20).astype(np.float32)
regression_target = np.random.rand(num_instances, 1).astype(np.float32)
classification_target = np.random.randint(5, size=num_instances)

auto_model.fit(
    [image_data, structured_data],
    [classification_target, regression_target],
    batch_size=32,
    epochs=3,
)

Can we load two input data with python generators and process each branch of them separately?
Can you transform this example to use generators for data that does not fit into memory ?
Thank you.