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,

        with:
          limit-access-to-actor: true
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      - name: Notify Translations
        run: uv run ./scripts/notify_translations.py
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          NUMBER: ${{ github.event.inputs.number || null }}

  // watched inputs.
  void Watch(const AbstractTensorHandle*);
  // Records an operation with given inputs and outputs
  // on the tape and marks all its outputs as watched if at
  // least one input of the op is watched and has a trainable dtype.
  // op_name is optional and is used for debugging only.
  void RecordOperation(absl::Span<AbstractTensorHandle* const> inputs,

  forward_op_->inputs.push_back(input);
  return absl::OkStatus();
}
absl::Status AddInputList(AbstractOperation* op_,
                          absl::Span<AbstractTensorHandle* const> inputs,
                          ForwardOperation* forward_op_) {
  TF_RETURN_IF_ERROR(op_->AddInputList(inputs));
  for (auto input : inputs) {
    forward_op_->inputs.push_back(input);
  }
  return absl::OkStatus();

//  ninputs - number of elements in `inputs` array
//  inputs - array of TF_Outputs that specify the inputs to the function.
//           If `ninputs` is zero (the function takes no inputs), `inputs`
//           can be null. The names used for function inputs are normalized
//           names of the operations (usually placeholders) pointed to by
//           `inputs`. These operation names should start with a letter.

                               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 =

    inputs.files(acceptedViolationsFiles)
    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)
    inputs.files(extractGradleApiInfo)

   * propagate cancellation) and {@link #toString()}. To access the futures' <i>values</i>, {@code
   * AggregateFuture} attaches listeners that hold references to one or more inputs. And in the case
   * of {@link CombinedFuture}, the user-supplied callback usually has its own references to inputs.
   */
  /*
   * In certain circumstances, this field might theoretically not be visible to an afterDone() call

                                    <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"

		var err error
		inputs, err = filepathx.Glob(flag.Args()[0])
		fatalErr(err)
		if len(inputs) == 0 {
			fmt.Println("Usage: No input found")
		}
	default:
		flag.Usage()
		fatalIf(true, "Only 1 file can be decrypted")
		os.Exit(1)
	}
	for _, input := range inputs {
		processFile(input, privateKeys)
	}
}

func processFile(inputFileName string, privateKeys [][]byte) {