value_string = "<error computing value>";
  }
  if (value_string.length() > 100) {
    // The default NumPy-style output can be distractingly long in error
    // messages.
    value_string = absl::StrCat(value_string.substr(0, 100), " [...]");
  }
  Status s;
  const char* device_name = DeviceName(&s);
  if (!s.ok()) {
    device_name = "<error fetching device name>";
  }

  // `string_view`
  if (length == read && content_view.substr(offset, length) ==
                            absl::string_view(result).substr(0, read))
    return ::testing::AssertionSuccess();
  else
    return ::testing::AssertionFailure()
           << "Result: " << absl::string_view(result).substr(0, read)
           << " Read: " << read;
}

      session->graph->sessions[session].clear();
    }

    const auto num_nodes = graph.num_node_ids();
    if (session->last_num_graph_nodes < num_nodes) {
      // TODO(nolivia): check this on a subset of the graph instead of all of
      // it.
      status->status = graph::ValidateGraphHasNoCycle(session->graph->graph);
      if (!status->status.ok()) {
        session->graph->mu.unlock();
        return false;

using tensorflow::string;

namespace tensorflow {
namespace {

static bool HasSubstr(absl::string_view base, absl::string_view substr) {
  bool ok = absl::StrContains(base, substr);
  EXPECT_TRUE(ok) << base << ", expected substring " << substr;
  return ok;
}

TEST(CAPI, MonitoringCounter0) {
  TF_Status* status = TF_NewStatus();
  auto* counter =

    TF_SetStatus(status, TF_INVALID_ARGUMENT,
                 "GCS path doesn't contain a bucket name.");
    return;
  }

  *bucket = fname.substr(scheme_end + 1, bucket_end - scheme_end - 1);
  *object = fname.substr(bucket_end + 1);

  if (object->empty() && !object_empty_ok) {
    TF_SetStatus(status, TF_INVALID_ARGUMENT,
                 "GCS path doesn't contain an object name.");
  }
}

  TF_Operation* func_feed = Placeholder(host_graph_, s_);
  TF_Operation* func_op = Use({func_feed, five});
  Run({{func_feed, Int32Tensor(2)}}, func_op, 2 /*+=*/ + 5 + 1);

  // Verify input, output, and subset of edges in fdef.
  // The subset of edges we verify is a chain between feed1 and output to
  // make sure that the correct output is picked.
  tensorflow::FunctionDef fdef;
  ASSERT_TRUE(GetFunctionDef(func_, &fdef));

    const char* device_name = TFE_OpGetDevice(matmul, status);
    ASSERT_TRUE(strstr(device_name, "GPU:0") != nullptr);

    TFE_OpSetDevice(matmul, "CPU:0", status);
    ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
    device_name = TFE_OpGetDevice(matmul, status);
    ASSERT_TRUE(strstr(device_name, "CPU:0") != nullptr);
  }

  TFE_DeleteOp(matmul);
  TFE_DeleteTensorHandle(m);