tensorflow::AttrValue attr_value;
  if (!attr_value.ParseFromArray(proto, proto_len)) {
    status->status = InvalidArgument("Unparseable AttrValue proto");
    return;
  }

  if (strcmp(attr_name, tensorflow::kColocationAttrName) == 0) {
    if (attr_value.value_case() != tensorflow::AttrValue::kList &&
        attr_value.value_case() != tensorflow::AttrValue::VALUE_NOT_SET) {
      status->status =

}

void TF_AbstractOpSetAttrType(TF_AbstractOp* op, const char* const attr_name,
                              TF_DataType value, TF_Status* s) {
  Status status =
      unwrap(op)->SetAttrType(attr_name, static_cast<DataType>(value));
  TF_SetStatus(s, static_cast<TF_Code>(status.code()),
               tsl::NullTerminatedMessage(status));
}

  TF_Tensor* result_tensor;
  s = GetValue(outputs[0], &result_tensor);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto result_value = static_cast<float*>(TF_TensorData(result_tensor));
  EXPECT_EQ(*result_value, 1.0);
  outputs[0]->Unref();
  TF_DeleteTensor(result_tensor);
  result_tensor = nullptr;
}

INSTANTIATE_TEST_SUITE_P(
    CustomGradientTest, CustomGradientTest,

void TF_AttrBuilderSetType(TF_AttrBuilder* builder, const char* attr_name,
                           TF_DataType value) {
  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, static_cast<tensorflow::DataType>(value));
}

void TF_AttrBuilderSetTypeList(TF_AttrBuilder* builder, const char* attr_name,

  } else {
    shape_string = "<error computing shape>";
  }
  std::string value_string;
  if (!SummarizeValue(value_string).ok()) {
    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;

                   ForwardOperation* forward_op_) {
  forward_op_->attrs.Set(attr_name, value);
  return op_->SetAttrBool(attr_name, value);
}
Status SetAttrType(AbstractOperation* op_, const char* attr_name,
                   DataType value, ForwardOperation* forward_op_) {
  forward_op_->attrs.Set(attr_name, value);
  return op_->SetAttrType(attr_name, value);
}

#include "tensorflow/c/experimental/filesystem/plugins/posix/posix_filesystem.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Register the POSIX filesystems statically.
// Return value will be unused
bool StaticallyRegisterLocalFilesystems() {
  TF_FilesystemPluginInfo info;
  TF_InitPlugin(&info);
  Status status = filesystem_registration::RegisterFilesystemPluginImpl(&info);
  if (!status.ok()) {

  DLTensor* dl_tensor = &dlmt->dl_tensor;
  absl::optional<std::string> device_name =
      DeviceNameFromDlContext(dl_tensor->device, status);
  if (!device_name.has_value()) {
    status->status =
        tensorflow::errors::InvalidArgument("Unsupported Device Type");
    return nullptr;
  }
  TF_DataType dtype;
  Status s = TfDataTypeFormDlDataType(dl_tensor->dtype, &dtype);

      "      attr {"
      "        key: 'dtype'"
      "        value {"
      "          type: DT_FLOAT"
      "        }"
      "      }"
      "    }"
      "    node_def {"
      "      name: 'add1'"
      "      op: 'Add'"
      "      input: 'read0:value:0'"
      "      input: 'read1:value:0'"
      "      attr {"
      "        key: 'T'"
      "        value {"
      "          type: DT_FLOAT"
      "        }"

    status->status = fdef_or.status();
    return;
  }

  (*(fdef_or.value()->mutable_attr()))[string(attr_name)] = attr_value;

  status->status = absl::OkStatus();
}

void TF_FunctionGetAttrValueProto(TF_Function* func, const char* attr_name,
                                  TF_Buffer* output_attr_value,
                                  TF_Status* status) {