}

    void assertHasImmediateCancel(
        ListenableFuture<String> a, ListenableFuture<String> b, Exception e) {
      ImmutableSet<ListenableFuture<String>> inputs = ImmutableSet.of(a, b);
      smartAssertTrue(inputs, e, intersect(inputs, ImmutableSet.of(doneCancelled)));
    }
  }

  /**

  }

  private static void assertMessageDigestHashing(byte[] input, String algorithmName) {
    try {
      MessageDigest digest = MessageDigest.getInstance(algorithmName);
      assertEquals(
          HashCode.fromBytes(digest.digest(input)), ALGORITHMS.get(algorithmName).hashBytes(input));
      for (int bytes = 4; bytes <= digest.getDigestLength(); bytes++) {
        assertEquals(

    }

    void assertHasImmediateCancel(
        ListenableFuture<String> a, ListenableFuture<String> b, Exception e) {
      ImmutableSet<ListenableFuture<String>> inputs = ImmutableSet.of(a, b);
      smartAssertTrue(inputs, e, intersect(inputs, ImmutableSet.of(doneCancelled)));
    }
  }

  /**

void Tape::RecordOperation(absl::Span<AbstractTensorHandle* const> inputs,
                           absl::Span<AbstractTensorHandle* const> outputs,
                           GradientFunction* gradient_function,
                           const string& op_name) {
  std::vector<int64_t> input_ids(inputs.size());
  std::vector<tensorflow::DataType> input_dtypes(inputs.size());
  for (int i = 0; i < inputs.size(); i++) {
    input_ids[i] = ToId(inputs[i]);

            final VaErrorHook hook) {
        return getEntity(form).map(entity -> {
            final String[] newInputs = splitLine(form.inputs);
            validateMappingString(action, newInputs, "inputs", hook);
            entity.setNewInputs(newInputs);
            final String newOutput = form.output;
            entity.setNewOutput(newOutput);
            return entity;
        });
    }

                 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;