AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs,
    absl::Span<AbstractTensorHandle*> outputs) {
  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);
  AbstractTensorHandle* neg_output;
  TF_RETURN_IF_ERROR(ops::Neg(ctx, inputs[0], &neg_output, "Neg"));
  tape.RecordOperation(inputs, {neg_output}, nullptr, "Neg");
  return tape.ComputeGradient(ctx,

    inputs.property('acceptedViolations', acceptedViolations.toAcceptedChangesMap())
    inputs.property("baseline.version", compatibilityBaselineVersion)
    inputs.property("currentVersion", baseVersion)
    def apiSourceFolders = configurations.currentSources.incoming.artifactView { lenient(true) }.files
    inputs.files("apiSourceFolders", apiSourceFolders)
    inputs.files(currentClasspath)

	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 {

                               absl::Span<AbstractTensorHandle* const> inputs,
                               int input_index, bool use_function,
                               AbstractTensorHandle** numerical_grad) {
  vector<AbstractTensorHandle*> theta_inputs(inputs.size());
  for (int i{}; i < inputs.size(); ++i) {
    theta_inputs[i] = inputs[i];
  }

  AbstractTensorHandle* theta =

      for (ClosingFuture<?> input : inputs) {
        input.becomeSubsumedInto(closeables);
      }
    }

    /**
     * Returns a new {@code ClosingFuture} pipeline step derived from the inputs by applying a
     * combining function to their values. The function can use a {@link DeferredCloser} to capture
     * objects to be closed when the pipeline is done.
     *

                                    <label for="inputs" class="col-sm-3 text-sm-right col-form-label"><la:message
                                            key="labels.dict_mapping_source"/></label>
                                    <div class="col-sm-9">
                                        <la:errors property="inputs"/>
                                        <la:textarea styleId="inputs" property="inputs" rows="5"

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).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will

    for (int i = 0; i < inputs.size(); i++) {
      GraphTensor* t = dyn_cast<GraphTensor>(inputs[i]);
      if (!t) {
        return tensorflow::errors::InvalidArgument(
            "Unable to cast input to GraphTensor");
      }
      tf_outputs[i] = t->output_;
    }
    TF_AddInputList(op_.get(), tf_outputs.data(), tf_outputs.size());
    return absl::OkStatus();
  }

	testCases := []struct {
		args       []string
		totalSizes []uint64
		indexes    [][]uint64
		success    bool
	}{
		// Invalid inputs.
		{
			[]string{"data{1...17}/export{1...52}"},
			[]uint64{14144},
			nil,
			false,
		},
		// Valid inputs.
		{
			[]string{"data{1...3}"},
			[]uint64{3},
			[][]uint64{{3}},
			true,
		},
		{

                        updater.write("# " + line);
                    }
                    continue;
                }

                inputs = m.group(1).trim().split(",");
                output = m.group(2).trim();

                if (inputs == null || output == null || inputs.length == 0) {
                    logger.warn("Failed to parse {} in {}", line, path);
                    if (updater != null) {