protected AbstractModelActionWithView(ModelReference<T> subject, ModelRuleDescriptor descriptor, List<? extends ModelReference<?>> inputs) {
        super(subject, descriptor, inputs);
    }

    @Override
    final public void execute(MutableModelNode node, List<ModelView<?>> inputs) {
        if (!node.isAtLeast(ModelNode.State.Created)) {

  // Condition function that returns input > 0.
  CondGraphBuilderFn cond_fn = [](const Scope& scope,
                                  const std::vector<Output>& inputs,
                                  Output* output) {
    DCHECK_EQ(inputs.size(), 1);
    *output = ops::Greater(scope, inputs[0], 0);
    return scope.status();
  };

  // Body function that subtracts one from input.

        }
        return inputs.build();
    }

    private static DataSource packSample(String name, List<DataSource> inputs, Packer packer, DataAccessor accessor) throws IOException {
        long sumLength = 0;
        for (DataSource input : inputs) {
            sumLength += input.getLength();
        }
        DataTarget target = accessor.createTarget(name, Level.Trial);
        packer.pack(inputs, target);

Under some circumstances, continuous build may not detect changes to inputs.

[[sec:continuous_build_missing_files]]
=== Creating input directories
Sometimes, creating an input directory that was previously missing does not trigger a build, due to the way file system watching works.
For example, creating the `src/main/java` directory may not trigger a build.

              NodeWith(Op("Add"), Name("add"),
                       Inputs(Out(NodeWith(Name("placeholder_a"))),
                              Out(NodeWith(Name("placeholder_b"))))));

  EXPECT_EQ(Explain(add.node(), NodeWith(Inputs())),
            "\nexpected 0 inputs but node has 2");
  EXPECT_EQ(
      Explain(add.node(), NodeWith(Inputs(Out(NodeWith(Name("blah"))), _))),

void CompareNumericalAndAutodiffGradients(
    Model model, Model grad_model, AbstractContext* ctx,
    absl::Span<AbstractTensorHandle* const> inputs, bool use_function,
    double abs_error) {
  auto num_inputs = inputs.size();
  std::vector<AbstractTensorHandle*> outputs(num_inputs);
  auto s = RunModel(grad_model, ctx, inputs, absl::MakeSpan(outputs),
                    /*use_function=*/use_function);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

// Creates parameters (placeholders) in the tracing `ctx` using the shape and
// dtype of `inputs`.
Status CreateParamsForInputs(AbstractContext* ctx,
                             absl::Span<AbstractTensorHandle* const> inputs,
                             std::vector<AbstractTensorHandle*>* params);

// A callable that takes tensor inputs and returns zero or more tensor outputs.
using Model = std::function<Status(AbstractContext*,

}

// tag::ad-hoc-task[]
tasks.register('processTemplatesAdHoc') {
    inputs.property('engine', TemplateEngineType.FREEMARKER)
    inputs.files(fileTree('src/templates'))
        .withPropertyName('sourceFiles')
        .withPathSensitivity(PathSensitivity.RELATIVE)
    inputs.property('templateData.name', 'docs')
    inputs.property('templateData.variables', [year: '2013'])

  return Run(FeedType{}, fetch_outputs, {}, outputs);
}

Status ClientSession::Run(const FeedType& inputs,
                          const std::vector<Output>& fetch_outputs,
                          std::vector<Tensor>* outputs) const {
  return Run(inputs, fetch_outputs, {}, outputs);
}

Status ClientSession::Run(const FeedType& inputs,
                          const std::vector<Output>& fetch_outputs,

        super(subject, descriptor, inputs);
        this.action = action;
    }

    public static <T> InputUsingModelAction<T> of(ModelReference<T> modelReference, ModelRuleDescriptor descriptor, List<ModelReference<?>> inputs, BiAction<? super T, ? super List<ModelView<?>>> initializer) {