/*device=*/""}});
}

template <typename T>
void ExpectEquals(AbstractTensorHandle* t, T expected) {
  TF_Tensor* result_t;
  Status s = tensorflow::GetValue(t, &result_t);
  ASSERT_TRUE(s.ok()) << s.message();
  auto value = static_cast<T*>(TF_TensorData(result_t));
  EXPECT_EQ(*value, expected);
  TF_DeleteTensor(result_t);
}

// TODO(srbs): Add tests for captures.

      GetVariableSnapshots(variables),
      /*pjrt_client=*/pjrt_client_, /*pjrt_device=*/*pjrt_device,
      /*use_pjrt_tensor_buffer=*/true, &exec_args,
      /*owned_args=*/{}, &non_donatable_input_indices));

  EXPECT_EQ(exec_args.size(), 2);

  std::shared_ptr<xla::Literal> literal1 = *exec_args[0]->ToLiteralSync();
  EXPECT_TRUE(xla::LiteralTestUtil::Equal(
      *literal1, xla::LiteralUtil::CreateR2<int32_t>({{1, 2, 3}})));

#include "tensorflow/core/platform/stacktrace_handler.h"
#include "tensorflow/core/platform/test.h"

#define ASSERT_TF_OK(x) ASSERT_EQ(TF_OK, TF_GetCode(x)) << TF_Message(x)
#define EXPECT_TF_OK(x) EXPECT_EQ(TF_OK, TF_GetCode(x)) << TF_Message(x)

static const char* content = "abcdefghijklmnopqrstuvwxyz1234567890";
// We will work with content_view instead of content.
static const absl::string_view content_view = content;

    TF_EXPECT_OK(
        session.Run({{x, {-3.0f, 0.0f, 3.0f}}}, grad_outputs, &grad_result));
    EXPECT_EQ(grad_result.size(), 1);
    EXPECT_EQ(grad_result[0].NumElements(), 3);
    EXPECT_EQ(grad_result[0].flat<float>()(0), 0.0f);
    EXPECT_EQ(grad_result[0].flat<float>()(1), 0.0f);
    EXPECT_EQ(grad_result[0].flat<float>()(2), 0.0f);
  }
}

TEST_F(NaryGradTest, SquaredDifference) {

  root.graph()->AddControlEdge(call, write_op);

  std::unique_ptr<Graph> graph;
  Status failure_status = BuildXlaOps(root, fdef_lib, &graph);
  ASSERT_FALSE(failure_status.ok());
  EXPECT_EQ(failure_status.code(), error::INVALID_ARGUMENT);
}

TEST_F(BuildXlaOpsTest, OnNonXlaDevice) {
  Scope root = Scope::NewRootScope().ExitOnError();

  FunctionDefLibrary fdef_lib =

  EXPECT_EQ(SavedModelReadApi("bar").value(), 1);
  SavedModelReadCount("2").IncrementBy(1);
  EXPECT_EQ(SavedModelReadCount("2").value(), 1);

  SavedModelReadApi("baz").IncrementBy(1);
  EXPECT_EQ(SavedModelReadApi("baz").value(), 1);
  SavedModelReadCount("2").IncrementBy(1);
  EXPECT_EQ(SavedModelReadCount("2").value(), 2);
}

TEST(MetricsTest, TestCheckpointRead) {

  const auto names1 = LinearOp(root);

  EXPECT_EQ(names1[0], "linear/mul");
  EXPECT_EQ(names1[1], "linear/bias_add");
  EXPECT_EQ(names1[2], "linear/c1");
  EXPECT_EQ(names1[3], "linear/mul_1");
  EXPECT_EQ(names1[4], "linear");

  EXPECT_EQ(root.GetUniqueNameForOp("linear"), "linear_1");

  const auto names2 = LinearOp(root);

  EXPECT_EQ(names2[0], "linear_2/mul");
  EXPECT_EQ(names2[1], "linear_2/bias_add");

  TF_EXPECT_OK(status.status);
  EXPECT_EQ(value, 1);
  EXPECT_EQ(num_compute_calls, 2);
  cache2.LookupOrCompute("c", &value, compute_func, &status);
  TF_EXPECT_OK(status.status);
  EXPECT_EQ(value, 2);
  EXPECT_EQ(num_compute_calls, 3);
  cache2.LookupOrCompute("d", &value, compute_func, &status);
  TF_EXPECT_OK(status.status);
  EXPECT_EQ(value, 3);
  EXPECT_EQ(num_compute_calls, 4);

  std::optional<CalibrationStatistics> statistics = collector.GetStatistics();

  EXPECT_TRUE(statistics.has_value());
  EXPECT_EQ(statistics.value().average_min_max_statistics().min_sum(), -4.0f);
  EXPECT_EQ(statistics.value().average_min_max_statistics().max_sum(), 15.0f);
  EXPECT_EQ(statistics.value().average_min_max_statistics().num_samples(), 2);
}

TEST(CalibrationStatisticsCollectorTest, ClearDataAndGetResultsMinMax) {

            }
        """

        and:
        module.jar.expectPut()
        module.jar.sha1.expectPut()
        module.jar.sha256.expectPut()
        module.jar.sha512.expectPut()
        module.ivy.expectPut()
        module.ivy.sha1.expectPut()
        module.ivy.sha256.expectPut()
        module.ivy.sha512.expectPut()

        when:
        run 'publish'

        then: