ASSERT_EQ(1, grads.size());
  ASSERT_EQ(func_name_, grads[0].first);
  ASSERT_EQ("MyGrad", grads[0].second);

  // These calls must be noops
  TF_GraphCopyFunction(host_graph_, func_, grad_func, s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  TF_GraphCopyFunction(host_graph_, func_, nullptr, s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  // Delete the gradient func.

  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));
  ASSERT_EQ(device_type, nullptr);
  ASSERT_EQ("Invalid handle", string(TF_Message(status.get())));

  TF_SetStatus(status.get(), TF_OK, "");

  int device_id = TFE_TensorHandleDeviceID(h, status.get());
  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));
  ASSERT_EQ(device_id, -1);

    ASSERT_EQ(errors::OK, s.code()) << s.message();
    s = dyn_cast<tracing::TracingContext>(ctx.get())->AddParameter(
        DT_FLOAT, shape, &x_raw);
    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();

  auto s = GetValue(t, &analytical_tensor);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

  int64_t num_elem_analytical = 1;
  auto num_dims_analytical = TF_NumDims(analytical_tensor);
  ASSERT_EQ(dims.size(), num_dims_analytical);
  for (int j = 0; j < num_dims_analytical; j++) {
    auto dim_analytical = TF_Dim(analytical_tensor, j);
    ASSERT_EQ(dims[j], dim_analytical);
    num_elem_analytical *= dim_analytical;

  TFE_Execute(func, &retvals[0], &num_retvals, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  ASSERT_EQ(1, num_retvals);
  TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteTensorHandle(retvals[0]);
  float sum = 0;
  ASSERT_EQ(sizeof(sum), TF_TensorByteSize(t));
  memcpy(&sum, TF_TensorData(t), TF_TensorByteSize(t));

    status_ = StatusFromTF_Status(status.get());
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();

    {
      AbstractContext* ctx_raw = nullptr;
      status_ =
          BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);
      ASSERT_EQ(errors::OK, status_.code()) << status_.message();
      immediate_execution_ctx_.reset(ctx_raw);
    }

  std::stringstream stream;
  stream << x;
  ASSERT_EQ(stream.str(), "42");
}

TEST(OStreamTest, TestFloat32) {
  Float32 x(0.375);  // Exactly representable as a float.
  std::stringstream stream;
  stream << x;
  ASSERT_EQ(stream.str(), "0.375");
}

TEST(OStreamTest, TestString) {
  String s("foo");
  std::stringstream stream;
  stream << s;
  ASSERT_EQ(stream.str(), "foo");
}

                  }
                }));
    assertGet(k0(), v0(), v3(), v4());
  }

  @CollectionSize.Require(absent = ZERO)
  @MapFeature.Require(SUPPORTS_PUT)
  public void testPutAllNonEmptyCollectionOnPresentKey() {
    assertTrue(multimap().putAll(k0(), Lists.newArrayList(v3(), v4())));
    assertGet(k0(), v0(), v3(), v4());
  }

  @MapFeature.Require(SUPPORTS_PUT)

                  }
                }));
    assertGet(k0(), v0(), v3(), v4());
  }

  @CollectionSize.Require(absent = ZERO)
  @MapFeature.Require(SUPPORTS_PUT)
  public void testPutAllNonEmptyCollectionOnPresentKey() {
    assertTrue(multimap().putAll(k0(), Lists.newArrayList(v3(), v4())));
    assertGet(k0(), v0(), v3(), v4());
  }

  @MapFeature.Require(SUPPORTS_PUT)

                          &numerical_grad_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    numerical_grad.reset(numerical_grad_raw);
  }

  TF_Tensor* numerical_tensor;
  s = GetValue(numerical_grad.get(), &numerical_tensor);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto num_elem_numerical = TF_TensorElementCount(numerical_tensor);
  ASSERT_EQ(num_elem_numerical, num_grad);