}

TEST(Grappler, MultiplePluginRegistration) {
  auto plugin_init_0 = [](TP_OptimizerRegistrationParams* const params,
                          TF_Status* const status) -> void {
    TF_SetStatus(status, TF_OK, "");
    PopulateDefaultParam(params);
    params->device_type = "Device0";
  };
  auto plugin_init_1 = [](TP_OptimizerRegistrationParams* const params,

//
// The code under test will have created streamz counters like this:
// auto* streamz = tensorflow::monitoring::Counter<1>::New("name",
// "description", "label");
// and then incremented that counter for various values of label:
// streamz->GetCell("label-value")->IncrementBy(1);
//
// The test code can then read and test the value of that counter:
//
// auto* reader = TFE_MonitoringNewCounterReader("name");
// test();

  *result = outputs[0];
  return absl::OkStatus();
}
}  // namespace

Status GradientRegistry::Register(
    const string& op_name, GradientFunctionFactory gradient_function_factory) {
  auto iter = registry_.find(op_name);
  if (iter != registry_.end()) {
    const string error_msg = "Gradient already exists for op: " + op_name + ".";
    return errors::AlreadyExists(error_msg);
  }

  auto core_filesystem_ops = CopyToCore<TF_FilesystemOps>(
      info->ops[index].filesystem_ops, info->ops[index].filesystem_ops_size);
  auto core_random_access_file_ops = CopyToCore<TF_RandomAccessFileOps>(
      info->ops[index].random_access_file_ops,
      info->ops[index].random_access_file_ops_size);
  auto core_writable_file_ops =

  forward_op_.inputs.push_back(input);
  return OkStatus();
}
Status TapeOperation::AddInputList(
    absl::Span<AbstractTensorHandle* const> inputs) {
  TF_RETURN_IF_ERROR(parent_op_->AddInputList(inputs));
  for (auto input : inputs) {
    forward_op_.inputs.push_back(input);
  }
  return OkStatus();
}
Status TapeOperation::SetAttrString(const char* attr_name, const char* data,
                                    size_t length) {

{
  "settings": {
    "index": {
      "refresh_interval": "1s",
      "number_of_shards": 1,
      "number_of_replicas": 0,
      "auto_expand_replicas": "0-1"
    }
  }

{
  "settings": {
    "index": {
      "refresh_interval": "1s",
      "number_of_shards": 1,
      "number_of_replicas": 0,
      "auto_expand_replicas": "0-1"
    }
  }

{
  "settings": {
    "index": {
      "refresh_interval": "1s",
      "number_of_shards": 5,
      "number_of_replicas": 0,
      "auto_expand_replicas": "0-1"
    }
  }

    CHECK_LT(output.index, ic->num_outputs());  // Crash OK
    const auto* shapes_and_types =
        ic->output_handle_shapes_and_types(output.index);
    if (shapes_and_types == nullptr) return nullptr;

    for (const auto& p : *shapes_and_types) {
      auto* out_shape_and_type = handle_data.add_shape_and_type();
      ic->ShapeHandleToProto(p.shape, out_shape_and_type->mutable_shape());

// destructor. The easiest way to use MakeCleanup is with a lambda argument,
// capturing the return value in an 'auto' local variable. Most users will not
// need more sophisticated syntax than that.
//
// Example:
//   void func() {
//     FILE* fp = fopen("data.txt", "r");
//     if (fp == nullptr) return;
//     auto fp_cleaner = gtl::MakeCleanup([fp] { fclose(fp); });
//     // No matter what, fclose(fp) will happen.