/*input_tensors*/ {},
                    /*expected_shape*/ make_shape({3, kUnknownDim}));

  // Infer shape when some dimensions are unknown.
  CheckOutputShapes(
      matmul_op,
      /*input_shapes*/ {make_shape({kUnknownDim, 2}), make_shape({2, 4})},
      /*input_tensors*/ {},
      /*expected_shape*/ make_shape({kUnknownDim, 4}));

  // Infer shape when everything is unknown.

    ASSERT_EQ(errors::OK, s.code()) << s.message();
    x.reset(x_raw);
  }

  PartialTensorShape shape;
  Status s = x->Shape(&shape);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  ASSERT_TRUE(shape.unknown_rank());
}

TEST_P(UnifiedAPI, TestPartialShapeTracing) {
  if (!UseFunction()) {
    GTEST_SKIP() << "Tracing only test.";
  }
  if (UseMlir()) {

  // the tensor handle can be an error and return non-OK status.
  virtual tensorflow::Status TensorHandleStatus() const;

  // Returns tensor shape. If tensor has unknown rank, shape remains untouched.
  virtual tensorflow::Status Shape(
      tensorflow::PartialTensorShape* shape) const = 0;

  // Returns tensor (full) type.

                                   " dimensions which is over the limit of ",
                                   TensorShape::MaxDimensions(), ".");
  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {
    for (int d = 0; d < num_dims; ++d) {
      proto.add_dim()->set_size(dims[d]);
    }
  }

  forward_op_.attrs.Set(attr_name, proto);

  // Returns number of elements across all dimensions.
  virtual Status NumElements(int64_t* num_elements) const = 0;
  // Returns size of specified dimension
  //
  // -1 indicates an unknown axis length; this is unreachable for most standard
  // ImmediateExecutionTensorHandles, but comes up for example when computing
  // the shape of a parallel tensor with component shapes differing across
  // devices.

void TF_DeleteExecutionContext(TF_ExecutionContext*);

// Represents a (partially-defined) shape.
typedef struct TF_Shape {
  int num_dims;  // Must be >= -1; -1 represents unknown rank.
  int64_t* dim_sizes;
} TF_Shape;

// Add a new parameter to a TensorFlow Function.
TF_AbstractTensor* TF_AddFunctionParameter(TF_ExecutionContext* func,

                                   " dimensions which is over the limit of ",
                                   TensorShape::MaxDimensions(), ".");
  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {
    for (int d = 0; d < num_dims; ++d) {
      proto.add_dim()->set_size(dims[d]);
    }
  }

  forward_op_->attrs.Set(attr_name, proto);

}

TEST(CAPI, StatusEnum) {
  EXPECT_EQ(TF_OK, static_cast<TF_Code>(tensorflow::error::OK));
  EXPECT_EQ(TF_CANCELLED, static_cast<TF_Code>(tensorflow::error::CANCELLED));
  EXPECT_EQ(TF_UNKNOWN, static_cast<TF_Code>(tensorflow::error::UNKNOWN));
  EXPECT_EQ(TF_INVALID_ARGUMENT,
            static_cast<TF_Code>(absl::StatusCode::kInvalidArgument));
  EXPECT_EQ(TF_DEADLINE_EXCEEDED,

echo -e "OUTPUT_FORMAT(elf64-littleaarch64)\nINPUT ( libstdc++.so.6.0.18 -lstdc++_nonshared44 )" \
   > "${TARGET}/usr/lib64/libstdc++.so.${LIBSTDCXX_VERSION}"
cp "./aarch64-unknown-linux-gnu/libstdc++-v3/src/.libs/libstdc++_nonshared44.a" \
   "${TARGET}/usr/lib64"


# Link in architecture specific includes from the system; note that we cannot

                                                                    &device)) {
    methods.deallocator(data);
    status->status =
        tensorflow::errors::InvalidArgument(device_name, " unknown device.");
    return nullptr;
  }
  return tensorflow::wrap(new tensorflow::CAPICustomDeviceTensorHandle(
      context, device, *reinterpret_cast<tensorflow::DataType*>(&dtype), data,
      methods));
}