// 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*,

  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,

                  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(

          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) {

            // TODO when implemented, this assertion should be replaced with the commented one:
            inputs.expect(allOf(startsWith("Plugin class 'SneakyPluginA': file '${FileUtils.testFileName}'"), containsString(FileUtils.testFileName)))
            /*
            pluginClasses.forEach {
                inputs = inputs.expect(allOf(startsWith("Plugin class '$it':"), containsString(FileUtils.testFileName))))
            }*/
        }

                           bool allow_mixing_unknown_and_cpu,
                           absl::Span<const absl::string_view> inputs) {
  string result;
  TF_ASSERT_OK(PickDeviceHelper(allow_mixing_unknown_and_cpu, inputs, &result))
      << "inputs = [" << absl::StrJoin(inputs, ", ")
      << "], allow_mixing_unknown_and_cpu=" << allow_mixing_unknown_and_cpu
      << ", expected_result=" << expected_result;

        "inputs.file('a')"                                          | "TaskInputs.file(Object)"
        "inputs.files('a')"                                         | "TaskInputs.files(Object...)"
        "inputs.dir('a')"                                           | "TaskInputs.dir(Object)"
        "inputs.property('key', 'value')"                           | "TaskInputs.property(String, Object)"

        (MHLO_CompositeOp:$composite
          (variadic $inputs),
          ConstantStrAttr<StrAttr, "aten.gelu.default">, $attrs, $_, $_),
        (TFL_GeluOp $inputs, ConstBoolAttrFalse),
        [(IsStrCompositeAttribute<"approximate", "none"> $attrs)]>;

def LegalizeCompositeApproximateAtenGELU : Pat<
        (MHLO_CompositeOp:$composite
          (variadic $inputs),