const int64_t* dims, const int num_dims,
                           const char* device, TF_Status* status) {
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(
      TFE_NewOp(context, "VarHandleOp", status), TFE_DeleteOp);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op.get(), "dtype", type);
  TFE_OpSetAttrShape(op.get(), "shape", dims, num_dims, status);

    ASSERT_NO_FATAL_FAILURE(CheckTensorValue(outputs[0], dA_vals[i],
                                             /*dims*/ {3, 3},
                                             /*abs_error*/ 0));
    ASSERT_NO_FATAL_FAILURE(CheckTensorValue(outputs[1], dB_vals[i],
                                             /*dims*/ {3, 3},
                                             /*abs_error*/ 0));
    outputs[0]->Unref();
    outputs[1]->Unref();

  static char empty;
  int64_t nelems = 1;
  std::vector<int64_t> dims;
  dims.reserve(shape.dims());
  for (int i = 0; i < shape.dims(); ++i) {
    dims.push_back(shape.dim_size(i));
    nelems *= shape.dim_size(i);
  }
  CHECK_EQ(nelems, 0);
  return TF_NewTensor(
      dtype, reinterpret_cast<const int64_t*>(dims.data()), shape.dims(),
      reinterpret_cast<void*>(&empty), 0, [](void*, size_t, void*) {}, nullptr);

    absl::Span<AbstractTensorHandle* const> inputs, bool use_function,
    double abs_error = 1e-2);

void CheckTensorValue(AbstractTensorHandle* t, absl::Span<const float> manuals,
                      absl::Span<const int64_t> dims, double abs_error = 1e-2);

Model BuildGradModel(Model forward, GradientRegistry registry);

}  // namespace internal
}  // namespace gradients
}  // namespace tensorflow

extern "C" {

TF_Shape* TF_NewShape() {
  return tensorflow::wrap(new tensorflow::PartialTensorShape());
}

int TF_ShapeDims(const TF_Shape* shape) {
  return tensorflow::unwrap(shape)->dims();
}

int64_t TF_ShapeDimSize(const TF_Shape* shape, int d) {
  return tensorflow::unwrap(shape)->dim_size(d);
}

void TF_DeleteShape(TF_Shape* shape) { delete tensorflow::unwrap(shape); }

  absl::StatusOr<xla::Literal> CreateRandomF32Literal(
      absl::Span<const int64_t> dims, float min = -100, float max = 100) {
    TF_ASSIGN_OR_RETURN(auto shape,
                        xla::ShapeUtil::MakeValidatedShape(xla::F32, dims));
    return xla::LiteralUtil::CreateLiteralWithGenerator<xla::F32, float>(
        shape, [this, min, max](absl::Span<const int64_t> dims) -> float {
          return absl::Uniform(bitgen_, min, max);
        });

      } else {
        const auto num_dims = tensor_shape.dim_size();
        std::unique_ptr<int64_t[]> dims(new int64_t[num_dims]);
        for (int i = 0; i < num_dims; ++i) {
          dims[i] = tensor_shape.dim(i).size();
        }
        TFE_OpSetAttrShape(op, attr_name, dims.get(), num_dims, status);
      }
    } break;
    case tensorflow::AttrValue::kFunc: {

def LowerFillOp : Pat<(TF_FillOp $dims, $value),
                      (TF_BroadcastToOp $value, $dims)>;

//===----------------------------------------------------------------------===//
// Empty op patterns.
//===----------------------------------------------------------------------===//

def LowerEmptyOp : Pat<(TF_EmptyOp:$result $dims, TrueBoolAttr:$init),
                      (TF_BroadcastToOp

                                                TF_Status* status) {
  ::tensorflow::TensorShape shape;
  auto* cc_ctx = reinterpret_cast<::tensorflow::OpKernelConstruction*>(ctx);
  ::tensorflow::Status s = cc_ctx->GetAttr(attr_name, &shape);
  ::tensorflow::Set_TF_Status_from_Status(status, s);
  size_t rank = static_cast<size_t>(shape.dims());

  if (!status->status.ok()) return;

  if (!shaped_type) return false;

  return shaped_type.hasStaticShape();
}

bool HasStaticShapeAtDims(Value value, const ArrayRef<int> dims) {
  auto shaped_type = mlir::dyn_cast<ShapedType>(value.getType());
  if (!shaped_type || !shaped_type.hasRank()) return false;

  for (auto dim : dims) {
    if (shaped_type.isDynamicDim(dim)) return false;
  }
  return true;
}