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) {

inline impl::NodeMatcherProperties Attr(const string& name) {
  return impl::Attr(name);
}

// Matches a node with inputs `inputs`.
//
// `inputs` are ordered; `inputs`[i] must match input i.
template <typename... Ts>
impl::NodeMatcherProperties Inputs(Ts... inputs) {
  return impl::Inputs({inputs...});
}

// Matches the `idx`'th output of a node that matches `node`.
::testing::Matcher<impl::OutEdge> Out(int oidx,

    }

    public Object get(String path) {
        PotentialInput potentialInput = inputs.get(path);
        if (potentialInput == null) {
            throw new IllegalStateException("no input for " + path);
        }
        return potentialInput.get(modelViews);
    }

	col3 := newSchema("col3")
	col4 := newSchema("col4")

	a1 := &analyzer{name: "a1", inputs: []config.GroupVersionKind{col1.GroupVersionKind()}}
	a2 := &analyzer{name: "a2", inputs: []config.GroupVersionKind{col2.GroupVersionKind()}}
	a3 := &analyzer{name: "a3", inputs: []config.GroupVersionKind{col3.GroupVersionKind()}}
	a4 := &analyzer{name: "a4", inputs: []config.GroupVersionKind{col4.GroupVersionKind()}}

	a := Combine("combined", a1, a2, a3, a4)

              dependsOn "a"
              inputs.dir "b"
              outputs.file "build/b"
              doLast {}
            }
            task c {
              dependsOn "b"
              inputs.dir "c"
              outputs.file "build/c"
              doLast {}
            }
            task d {
              dependsOn "c"
              inputs.dir "d"
              outputs.file "build/d"

                  const std::vector<Output>& inputs,
                  std::vector<Output>* outputs) {
  DCHECK(outputs != nullptr);
  DCHECK(outputs->empty());

  // The control dependency is analogous to that in CreateCond().
  Scope body_scope =
      scope.NewSubScope("body").WithControlDependencies(inputs[0]);
  TF_RETURN_IF_ERROR(body(body_scope, inputs, outputs));

  const size_t num_loop_vars = inputs.size();

  bool UseFunction() const { return std::get<2>(GetParam()); }
};

// Checks that inputs[0] is a scalar.
Status TestScalarShape(AbstractContext* ctx,
                       absl::Span<AbstractTensorHandle* const> inputs,
                       absl::Span<AbstractTensorHandle*> outputs) {
  PartialTensorShape shape;
  TF_RETURN_IF_ERROR(inputs[0]->Shape(&shape));
  if (shape.dims() != 0) {
    return errors::InvalidArgument(

        inputsDir.file('4.txt').asFile.text = 'Content for new file 4.'
    }
}
// end::updated-inputs[]

// tag::removed-input[]
tasks.register('removeInput', Delete) {
    delete 'inputs/3.txt'
}
// end::removed-input[]

// tag::removed-output[]
tasks.register('removeOutput', Delete) {
    delete layout.buildDirectory.file("outputs/1.txt")
}
// end::removed-output[]

          A result(Collector<T, A, R> collector, Iterable<T> inputs) {
        List<A> stack = new ArrayList<>();
        for (T input : inputs) {
          A newAccum = collector.supplier().get();
          collector.accumulator().accept(newAccum, input);
          push(stack, newAccum);
        }
        push(stack, collector.supplier().get());

    return absl::OkStatus();
  }
};

// Computes:
//
// @tf.custom_gradient
// def f(input):
//   def grad(grads):
//     return grads[0]
//   return tf.exp(input), grad
// outputs = [f(inputs[0])]
Status ExpWithPassThroughGrad(AbstractContext* ctx,
                              absl::Span<AbstractTensorHandle* const> inputs,
                              absl::Span<AbstractTensorHandle*> outputs) {