Christian Wansart <******@****.***> 1573139799 +0100

  EXPECT_EQ(tensor.dims(), 1);
  EXPECT_EQ(tensor.dtype(), dtype);
  absl::Span<const typename TypeParam::type> tensor_view(
      reinterpret_cast<typename TypeParam::type*>(tensor.data()), value.size());
  EXPECT_EQ(tensor_view[0], 42);
  EXPECT_EQ(tensor_view[1], 100);
  EXPECT_EQ(tensor_view[2], 0);
  EXPECT_EQ(tensor_view[3], 1);
  EXPECT_EQ(tensor_view[4], 4);

    InstantiationResultForTest result;
    TF_EXPECT_OK(InstantiateFunctionForTest(function, flib_def, &result));

    EXPECT_EQ((DataTypeVector{DT_INT32, DT_FLOAT, DT_INT32, DT_FLOAT,
                              DT_RESOURCE, DT_RESOURCE, DT_RESOURCE}),
              result.arg_types);
    EXPECT_EQ((DataTypeVector{DT_FLOAT, DT_INT32, DT_FLOAT, DT_FLOAT}),
              result.ret_types);

Christian Wansart <******@****.***> 1573139799 +0100

                  .ok());
  ASSERT_TRUE(worker_server2->Start().ok());

  TFE_ContextSetServerDef(context.get(), 0, serialized.data(),
                          serialized.size(), status.get());
  EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  BasicTestsForTwoDevices(context.get(),
                          "/job:worker/replica:0/task:1/device:CPU:0",

  TF_EXPECT_OK(CompileWithComputation(function_to_hlo_args).status());

  Histogram histogram =
      compilation_time.Delta("graph_old_bridge_has_function_to_hlo");

  EXPECT_EQ(histogram.num(), 1);
  EXPECT_EQ(compilation_status.Delta("kOldBridgeNoMlirSuccess"), 1);
}

TEST_F(CompileTFGraphTest, SuccessfullyCompilesWithManualSharding) {
  // MLIR module from failing test.

void Verify(const RuntimeMetadata* result, const RuntimeMetadata* expected) {
  EXPECT_EQ(result->subgraph_metadata()->size(),
            expected->subgraph_metadata()->size());
  for (int i = 0; i < result->subgraph_metadata()->size(); ++i) {
    auto result_subgraph_metadata =
        result->subgraph_metadata()->GetAs<SubgraphMetadata>(i);
    auto expected_subgraph_metadata =
        expected->subgraph_metadata()->GetAs<SubgraphMetadata>(i);

  reader.parse(metrics::SavedModelReadFingerprint().value(), fingerprint);
  EXPECT_EQ(fingerprint["saved_model_checksum"].asUInt64(),
            15788619162413586750ULL);
  EXPECT_EQ(fingerprint["graph_def_program_hash"].asUInt64(),
            706963557435316516ULL);
  EXPECT_EQ(fingerprint["signature_def_hash"].asUInt64(),
            5693392539583495303ULL);
  EXPECT_EQ(fingerprint["saved_object_graph_hash"].asUInt64(),

  ASSERT_TRUE(out != nullptr);
  EXPECT_EQ(TF_FLOAT, TF_TensorType(out));
  EXPECT_EQ(2, TF_NumDims(out));
  EXPECT_EQ(4, TF_Dim(out, 0));
  EXPECT_EQ(1, TF_Dim(out, 1));
  float* values = static_cast<float*>(TF_TensorData(out));
  // These values are defined to be (input / 2) + 2.
  EXPECT_EQ(2, values[0]);
  EXPECT_EQ(2.5, values[1]);
  EXPECT_EQ(3, values[2]);
  EXPECT_EQ(3.5, values[3]);

				}
				if !strings.Contains(err.Error(), tc.ExpectErr) {
					t.Fatalf("expected error %s, got %v", tc.ExpectErr, err)
				}
				return
			}
			if len(tc.ExpectErr) > 0 {
				t.Fatalf("expected error %s, got none", tc.ExpectErr)
			}
			if !reflect.DeepEqual(tc.Out, tc.ExpectOut) {
				t.Fatalf("unexpected result:\n %s", cmp.Diff(tc.ExpectOut, tc.Out))
			}
		})
	}