void TF_DeleteOutputList(TF_OutputList* o) { delete unwrap(o); }
void TF_OutputListSetNumOutputs(TF_OutputList* o, int num_outputs,
                                TF_Status* s) {
  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();
}

        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

Status GradientRegistry::Register(

  }
  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) {

      AbstractFunction* func = nullptr;
      OutputList output_list;
      output_list.expected_num_outputs = 0;
      output_list.outputs.reserve(outputs.size());
      for (int i = 0; i < model_outputs.size(); i++) {
        if (model_outputs[i]) {
          output_list.outputs.emplace_back(model_outputs[i]);
          output_list.expected_num_outputs += 1;
        } else {
          null_indices.insert(i);
        }
      }

                            attributes, *num_outputs, status));
  if (TF_GetCode(status) != TF_OK) return;
  if (!maybe_typed_outputs.has_value()) {
    TF_SetStatus(status, TF_INTERNAL, "OK status but no value was returned.");
    return;
  }

  std::vector<MaybeParallelTensorOwned> typed_outputs(
      std::move(maybe_typed_outputs.value()));

  if (typed_outputs.size() > *num_outputs) {
    TF_SetStatus(status, TF_INTERNAL,

  }

  TFE_Execute(op_.get(), unwrapped_results.data(), &real_num_outputs, status);
  if (TF_GetCode(status) != TF_OK) {
    cancellation_manager_->StartCancel();
    return;
  }
  unwrapped_results.resize(real_num_outputs);
  outputs->reserve(real_num_outputs);
  for (TFE_TensorHandle* unwrapped_result : unwrapped_results) {
    outputs->emplace_back(unwrapped_result);
  }

    if (TF_GetCode(status) != TF_OK) return nullptr;
    TFE_OpSetAttrType(const_op.get(), "dtype", datatype_enum);
    TFE_TensorHandle* device_handle;
    int num_outputs = 1;
    TFE_Execute(const_op.get(), &device_handle, &num_outputs, status);
    if (TF_GetCode(status) != TF_OK) return nullptr;
    components.emplace_back(device_handle);
  }
  return ParallelTensor::FromTensorHandles(*this, std::move(components),

#include "tensorflow/core/platform/types.h"

namespace tensorflow {

// Represents the results of the execution of an operation.
struct OutputList {
  std::vector<AbstractTensorHandle*> outputs;
  int expected_num_outputs = -1;
};

namespace tracing {

// =============================================================================
// Implementation detail for the unified execution APIs for Eager and tracing
// backends (graph/MLIR).

        // nodes must have a single output.
        if (node->num_outputs() != 1) {
          return InvalidArgument(
              "When `num_opers` is set to -1, nodes referenced in `inputs` "
              "must have a single output. Node ",
              node->name(), " has ", node->num_outputs(),
              " outputs. Encountered while creating function '", fn_name, "'");
        }
      }