TF_Buffer* result = TF_NewBuffer();
  const std::string& value_str = *value;
  void* data = malloc(value_str.length());
  value_str.copy(static_cast<char*>(data), value_str.length(), 0);
  result->data = data;
  result->length = value_str.length();
  result->data_deallocator = [](void* data, size_t length) { free(data); };
  return result;
}

TF_Buffer* TF_CoordinationServiceGetKeyValue(const char* key, int64_t key_size,

      } else {
        // Generalize differing axis lengths to "variable"/"unknown".
        for (int axis_index = 0; axis_index < combined_shape.dims();
             ++axis_index) {
          int64_t axis_length = combined_shape.dim_size(axis_index);
          if (axis_length != component_shape.dim_size(axis_index)) {
            axis_length = -1;
          }
          TF_RETURN_IF_ERROR(

  for (const std::string& str : nodes) {
    *storage_size += str.size();
  }
}

void TF_GetNodesToPreserveList(const TF_GrapplerItem* item, char** values,
                               size_t* lengths, int num_values, void* storage,
                               size_t storage_size, TF_Status* status) {
  TF_SetStatus(status, TF_OK, "");
  const std::unordered_set<std::string>& nodes =

  ASSERT_EQ(errors::OK, s.code()) << s.message();
  string message = "This is the way!";
  s = SetAttrString(check_numerics_op.get(), "message", message.data(),
                    message.length(), &forward_op);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  int num_retvals = 1;
  std::vector<AbstractTensorHandle*> outputs(1);
  GradientRegistry registry;
  s = RegisterGradients(&registry);

      TF_NewFunctionLibraryDefinition(g_buf, status);

  TF_LookUpOpDef(func, "Add", op_buf, status);
  string actual_string(reinterpret_cast<const char*>(op_buf->data),
                       op_buf->length);
  ASSERT_EQ(TF_OK, TF_GetCode(status));

  const OpDef* expected_op_def;
  TF_ASSERT_OK(OpRegistry::Global()->LookUpOpDef("Add", &expected_op_def));
  string expected_serialized;

  // API.
  std::string no_sharing = "cd2c89b7-88b7-44c8-ad83-06c2a9158347";
  TFE_OpSetAttrString(op.get(), "shared_name", no_sharing.c_str(),
                      no_sharing.length());
  TFE_OpSetDevice(op.get(), device, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_TensorHandle* var_handle = nullptr;
  int num_retvals = 1;
  TFE_Execute(op.get(), &var_handle, &num_retvals, status);

  tensorflow::string value = cell->cell.value();
  void* data = tensorflow::port::Malloc(value.length());
  value.copy(static_cast<char*>(data), value.length(), 0);
  buf->data = data;
  buf->length = value.length();
  buf->data_deallocator = [](void* data, size_t length) {
    tensorflow::port::Free(data);
  };
}

TFE_MonitoringStringGauge0* TFE_MonitoringNewStringGauge0(

      (TF_GetCode(status) != TF_OUT_OF_RANGE)) {
    return -1;
  }
  int64_t read;
  auto content_length = stream.headers().find("content-length");
  if (content_length == stream.headers().end()) {
    // When we read a file with offset that is bigger than the actual file size.
    // GCS will return an empty header (e.g no `content-length` header). In this
    // case, we will set read to `0` and continue.
    read = 0;

    }
    return absl::OkStatus();
  }

  Status SetAttrString(const char* attr_name, const char* data,
                       size_t length) override {
    tensorflow::StringPiece s(data, length);
    op_->node_builder.Attr(attr_name, s);
    return absl::OkStatus();
  }
  Status SetAttrInt(const char* attr_name, int64_t value) override {

    return errors::FailedPrecondition(
        "Trying to register filesystem without `data` operation on read only "
        "memory regions");

  if (ops->length == nullptr)
    return errors::FailedPrecondition(
        "Trying to register filesystem without `length` operation on read only "
        "memory regions");

  return OkStatus();
}

// Validates the operations supplied by the plugin.
//