inputs.add(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();
        }

Another common, but less obvious source of unstable inputs is when a task consumes the output of another task which produces non-repeatable results, such as the example before of a code generator that embeds timestamps in its output.

A task can only be loaded from the cache if it has stable task inputs.
Unstable task inputs result in the task having a unique set of inputs for every build, which will always result in a cache miss.

=== Build cycles
Gradle starts watching for changes just before a task executes.
If a task modifies its own inputs while executing, Gradle will detect the change and trigger a new build.
If every time the task executes, the inputs are modified again, the build will be triggered again.
This isn't unique to continuous build.
A task that modifies its own inputs will never be considered up-to-date when run "normally" without continuous 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"))), _))),

// 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*,

                    inputs = new String[inputStrings.size()];
                    for (int i = 0; i < inputs.length; i++) {
                        inputs[i] = unescape(inputStrings.get(i)).trim();
                    }

                    if (inputs.length > 0) {
                        id++;
                        final SynonymItem item = new SynonymItem(id, inputs, inputs);

	for i = 0; i < 8; i++ {
		fi := FileInfo{
			TransitionTier:      inputs[0].tier,
			TransitionedObjName: inputs[0].remoteObjName,
			TransitionVersionID: inputs[0].remoteVersionID,
			TransitionStatus:    inputs[0].status,
		}
		ofi := fi
		if i&(1<<0) != 0 {
			ofi.TransitionTier = inputs[1].tier
		}
		if i&(1<<1) != 0 {
			ofi.TransitionedObjName = inputs[1].remoteObjName
		}
		if i&(1<<2) != 0 {

    %0 = func.call @func_0_CPU_QUANTIZED_INT8(%arg0, %arg1, %arg2) {tac.device = "CPU", tac.inference_type = "QUANTIZED_INT8", tac.interface_name = "func_0"} : (tensor<100x!quant.uniform<i8:f32,...

                  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(