import deepchem.models.tensorgraph.layers as layers import deepchem.models.tensorgraph.model as model from deepchem.models.tensorgraph.tensor_graph import TensorGraph # build the model tg = TensorGraph() inputs = layers.Feature(shape=(None, num_features)) output = layers.Dense(num_classes, activation_fn=None)(inputs) tg.add_output(output) model = model.MultitaskModel(tg, [task_name]) # train the model model.fit(train_dataset) # save the model model.save("model.pb")In this example, we first import the necessary modules from the DeepChem library. We then create a `TensorGraph` object and define the input and output layers using `layers.Feature` and `layers.Dense`, respectively. We then create a `MultitaskModel` object and fit it on the training dataset before saving the model using the `save` method. The `DeepChem` library is a Python package designed for deep learning applications in drug discovery and materials science.