*
     * @return The new output stem.
     */
    public String getNewOutput() {
        return newOutput;
    }

    /**
     * Sets the new output stem.
     *
     * @param newOutputs The new output stem.
     */
    public void setNewOutput(final String newOutputs) {
        newOutput = newOutputs;
    }

    /**
     * Gets the original input word.
     *
     * @return The original input word.

     */
    public String[] getNewOutputs() {
        return newOutputs == null ? null : newOutputs.clone();
    }

    /**
     * Sets the new output synonyms.
     *
     * @param newOutputs The new output synonyms.
     */
    public void setNewOutputs(final String[] newOutputs) {
        this.newOutputs = newOutputs;
    }

    /**
     * Gets the original input words.

    public String getNewOutput() {
        return newOutput;
    }

    /**
     * Sets the new output character sequence for update operations.
     * Newline characters in the output are automatically replaced with spaces.
     *
     * @param newOutput the new output sequence to set
     */
    public void setNewOutput(final String newOutput) {
        this.newOutput = newOutput == null ? null : newOutput.replace("\n", " ");

        absl::StrCat("ZerosLike", ToId(t)).c_str()));
  }
  TF_RETURN_IF_ERROR(op->AddInput(t));
  int num_outputs = 1;
  std::vector<AbstractTensorHandle*> outputs(num_outputs);
  TF_RETURN_IF_ERROR(
      op->Execute(absl::Span<AbstractTensorHandle*>(outputs), &num_outputs));
  *result = outputs[0];
  return absl::OkStatus();
}
}  // namespace

absl::Status GradientRegistry::Register(

        item.setNewOutput("newOutput");

        String str = item.toString();
        assertTrue(str.contains("id=1"));
        assertTrue(str.contains("inputs=[a, b]"));
        assertTrue(str.contains("output=output"));
        assertTrue(str.contains("newInputs=[x, y]"));
        assertTrue(str.contains("newOutput=newOutput"));
    }

    @Test

  }
  std::vector<TFE_TensorHandle*> op_outputs(*num_outputs);
  TFE_Execute(op, op_outputs.data(), num_outputs, s);
  TFE_DeleteOp(op);
  if (TF_GetCode(s) != TF_OK) return;
  std::vector<TFE_TensorHandle*> unwrapped_outputs;
  unwrapped_outputs.reserve(op_outputs.size());
  for (auto* handle : op_outputs) {
    unwrapped_outputs.push_back(handle);
  }
  for (int i = 0; i < *num_outputs; ++i) {

          shape.dims()));
    }
    int num_outputs = 1;
    std::vector<AbstractTensorHandle*> outputs(num_outputs);
    TF_RETURN_IF_ERROR(operation->Execute(
        absl::Span<AbstractTensorHandle*>(outputs), &num_outputs));

    if (num_outputs != 1) {
      return errors::Internal("Expected 1 output but found ", num_outputs);
    }
    auto* t = dyn_cast<GraphTensor>(outputs[0]);
    if (!t) {

            validateSynonymString(action, newInputs, "inputs", hook);
            entity.setNewInputs(newInputs);
            final String[] newOutputs = splitLine(form.outputs);
            validateSynonymString(action, newOutputs, "outputs", hook);
            entity.setNewOutputs(newOutputs);
            return entity;
        });
    }

    // ===================================================================================

            validateMappingString(action, newInputs, "inputs", hook);
            entity.setNewInputs(newInputs);
            final String newOutput = form.output;
            entity.setNewOutput(newOutput);
            return entity;
        });
    }

    // ===================================================================================

  status->status = c.Run(op_reg_data->shape_inference_fn);
  if (!status->status.ok()) return;

  // Set output_shapes.
  TF_ShapeAndTypeList* output_shapes_result =
      TF_NewShapeAndTypeList(c.num_outputs());
  for (int i = 0; i < c.num_outputs(); ++i) {
    ShapeHandle shape_handle = c.output(i);
    TF_ShapeAndType& shape = output_shapes_result->items[i];
    shape.num_dims = c.Rank(shape_handle);