//  noutputs - number of elements in `outputs` array
//  outputs - array of TF_Outputs that specify the outputs of the function.
//            If `noutputs` is zero (the function returns no outputs), `outputs`
//            can be null. `outputs` can contain the same tensor more than once.
//  output_names - The names of the function's outputs. `output_names` array

  int num_retvals = 1;
  std::vector<AbstractTensorHandle*> outputs(1);
  GradientRegistry registry;
  s = RegisterGradients(&registry);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto tape = std::make_unique<Tape>(/*persistent=*/false);
  s = Execute(check_numerics_op.get(), ctx.get(), absl::MakeSpan(outputs),
              &num_retvals, &forward_op, tape.get(), registry);

        }
    }

    /**
     * Outputs INFO information.
     *
     * @param message
     *            Message
     */
    public void info(final Object message) {
        if (isInfoEnabled()) {
            log.info(toString(message));
        }
    }

    /**
     * Outputs WARN information.
     *
     * @param message
     *            Message

  int num_dims_out = TF_NumDims(model_out_tensor);
  TF_DeleteTensor(model_out_tensor);

  // If the output is a scalar, then return the scalar output
  if (num_dims_out == 0) {
    outputs[0] = model_out.release();
    return absl::OkStatus();
  }

  // Else, reduce sum the output to get a scalar

  // Will sum all dimensions, so get a Tensor containing [0,...,num_dims_out-1].
  AbstractTensorHandlePtr sum_dims;

	if [[ $? -eq 0 ]]; then
		echo "Outputs are consistent."
		break
	fi

	remaining_attempts=$((max_wait_attempts - attempt))
	if ((attempt >= max_wait_attempts)); then
		echo "Outputs remain inconsistent after $max_wait_attempts attempts. Exiting with error."
		exit 1
	else
		echo "Outputs are inconsistent. Waiting for $wait_interval seconds (attempt $attempt/$max_wait_attempts)."

    }
    inputs_.push_back(t->output_);
    *output = tensorflow::down_cast<TracingTensorHandle*>(outputs[0]);
    return absl::OkStatus();
  }

  absl::Status Finalize(OutputList* outputs, AbstractFunction** f) override {
    std::vector<TF_Output> graph_outputs;
    graph_outputs.reserve(outputs->outputs.size());
    for (auto* abstract_output : outputs->outputs) {
      GraphTensor* output = dyn_cast<GraphTensor>(abstract_output);

        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(

                         const std::vector<IOSpec>& outputs) {
    const OpDef& signature = fdef.signature();
    ASSERT_EQ(outputs.size(), signature.output_arg_size());
    for (int i = 0; i < outputs.size(); ++i) {
      const OpDef::ArgDef& arg = signature.output_arg(i);
      const IOSpec& out = outputs[i];
      if (out.second != DT_INVALID) {
        ASSERT_EQ(arg.type(), out.second)

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

  unwrap(o)->expected_num_outputs = num_outputs;
  unwrap(o)->outputs.clear();
  unwrap(o)->outputs.resize(num_outputs);
}
int TF_OutputListNumOutputs(TF_OutputList* o) {
  return unwrap(o)->outputs.size();
}
TF_AbstractTensor* TF_OutputListGet(TF_OutputList* o, int i) {
  return wrap(unwrap(o)->outputs[i]);
}
void TF_OutputListPushBack(TF_OutputList* o, TF_AbstractTensor* tensor,