absl::Span<AbstractTensorHandle*> grad_inputs) override {
    CHECK_EQ(grad_outputs.size(), 1);
    CHECK_EQ(grad_inputs.size(), 1);
    grad_inputs[0] = grad_outputs[0];
    if (grad_inputs[0]) {
      grad_inputs[0]->Ref();
    }
    return absl::OkStatus();
  }
};

// Computes:
//
// @tf.custom_gradient
// def f(input):
//   def grad(grads):
//     return grads[0]

                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    for (int i = 0; i < grad_outputs.size(); i++) {
      auto grad_input = grad_outputs[i];
      // TODO(srbs): Should we add a copy contructor to AbstractTensorHandle
      // that takes care of this similar to `Tensor`?
      if (grad_input) {
        grad_input->Ref();
      }
      grad_inputs[i] = grad_input;
    }
    return absl::OkStatus();
  }

      Iterable<? extends T>... inputs) {
    return concatNoDefensiveCopy(Arrays.copyOf(inputs, inputs.length));
  }

  /**
   * Returns a fluent iterable that combines several iterables. The returned iterable has an
   * iterator that traverses the elements of each iterable in {@code inputs}. The input iterators
   * are not polled until necessary.
   *

              "internal_listener": {},
              "filter_chain_matcher": {
                "matcher_tree": {
                  "input": {
                    "name": "ip",
                    "typed_config": {
                      "@type": "type.googleapis.com/envoy.extensions.matching.common_inputs.network.v3.DestinationIPInput"
                    }
                  },
                  "custom_match": {

  if (TF_GetCode(s) != TF_OK) return;
  int num_inputs = TFE_OpGetFlatInputCount(original_op, s);
  if (TF_GetCode(s) != TF_OK) return;
  for (int j = 0; j < num_inputs; ++j) {
    TFE_TensorHandle* input = TFE_OpGetFlatInput(original_op, j, s);
    if (TF_GetCode(s) != TF_OK) return;
    const char* input_device = TFE_TensorHandleDeviceName(input, s);
    if (TF_GetCode(s) != TF_OK) return;

// GetSubSys - extracts subssystem info from given config string
func GetSubSys(s string) (subSys string, inputs []string, tgt string, e error) {
	tgt = Default
	if len(s) == 0 {
		return subSys, inputs, tgt, Errorf("input arguments cannot be empty")
	}
	inputs = strings.SplitN(s, KvSpaceSeparator, 2)

	subSystemValue := strings.SplitN(inputs[0], SubSystemSeparator, 2)
	subSys = subSystemValue[0]
	if !SubSystems.Contains(subSys) {

namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and
 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *

      Iterable<? extends T>... inputs) {
    return concatNoDefensiveCopy(Arrays.copyOf(inputs, inputs.length));
  }

  /**
   * Returns a fluent iterable that combines several iterables. The returned iterable has an
   * iterator that traverses the elements of each iterable in {@code inputs}. The input iterators
   * are not polled until necessary.
   *

                    assertThat(peeker.getDone(input1)).isSameInstanceAs("value1");
                    try {
                      peeker.getDone(input2Failed);
                      fail("Peeker should fail for failed inputs");
                    } catch (ExecutionException expected) {
                    }
                    try {
                      peeker.getDone(nonInput);

              "internal_listener": {},
              "filter_chain_matcher": {
                "matcher_tree": {
                  "input": {
                    "name": "ip",
                    "typed_config": {
                      "@type": "type.googleapis.com/envoy.extensions.matching.common_inputs.network.v3.DestinationIPInput"
                    }
                  },
                  "custom_match": {