metadata.type = TF_ATTR_INT;
          } else if (typestr == "list(float)") {
            metadata.type = TF_ATTR_FLOAT;
          } else if (typestr == "list(bool)") {
            metadata.type = TF_ATTR_BOOL;
          } else if (typestr == "list(type)") {
            metadata.type = TF_ATTR_TYPE;
          } else if (typestr == "list(shape)") {
            metadata.type = TF_ATTR_SHAPE;

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  const DLTensor* dltensor_out = &dlm_out->dl_tensor;
  EXPECT_EQ(dltensor_out->device.device_type, dltensor_in->device.device_type);
  EXPECT_EQ(dltensor_out->device.device_id, dltensor_in->device.device_id);
  EXPECT_EQ(dltensor_out->ndim, dltensor_in->ndim);
  EXPECT_EQ(dltensor_out->dtype.code, dltensor_in->dtype.code);

                           TF_Status* s) {
  unwrap(o)->outputs.push_back(unwrap(tensor));
}

void TF_AbstractOpSetOpType(TF_AbstractOp* op, const char* const op_type,
                            TF_Status* s) {
  tsl::Set_TF_Status_from_Status(
      s, unwrap(op)->Reset(op_type,
                           /*raw_device_name=*/nullptr));
}

void TF_AbstractOpSetOpName(TF_AbstractOp* op, const char* const op_name,

}

void TF_AttrBuilderCheckCanRunOnDevice(TF_AttrBuilder* builder,
                                       const char* device_type,
                                       TF_Status* status) {
  status->status = tensorflow::FindKernelDef(
      tensorflow::DeviceType(device_type), builder->BuildNodeDef(),
      /* def = */ nullptr, /* kernel_class_name = */ nullptr);
}

    if (n > INT32_MAX)
      requested_read_length = INT32_MAX;
    else
      requested_read_length = n;

    // `pread` returns a `ssize_t` on POSIX, but due to interface being
    // cross-platform, return type of `Read` is `int64_t`.
    int64_t r = int64_t{pread(posix_file->fd, dst, requested_read_length,
                              static_cast<off_t>(offset))};
    if (r > 0) {
      dst += r;

  std::vector<int32_t> data(1, 0);
  xla::Shape shape = xla::ShapeUtil::MakeShape(xla::S32, {1});

  auto buffer = c_api_client->pjrt_c_client()->client->BufferFromHostBuffer(
      data.data(), shape.element_type(), shape.dimensions(),
      /*byte_strides=*/std::nullopt,
      xla::PjRtClient::HostBufferSemantics::kImmutableOnlyDuringCall, nullptr,
      c_api_client->pjrt_c_client()->client->addressable_devices()[0]);

      tensorflow::DeviceType(device_type), std::move(*pjrt_client));
  status->status = s;
}

void TF_ResetPjRtCClient(const char* device_type, TF_Status* status) {
  status->status =
      tensorflow::ResetPjRtClient(tensorflow::DeviceType(device_type));
}

PJRT_Client* TF_GetPjRtCClient(const char* device_type, TF_Status* status) {
  absl::StatusOr<xla::PjRtCApiClient*> pjrt_c_api_client =

      "    signature {"
      "      name: 'AddVariablesFunction'"
      "      input_arg {"
      "        name: 'var'"
      "        type: DT_RESOURCE"
      "      }"
      "      output_arg {"
      "        name: 'sum'"
      "        type: DT_FLOAT"
      "      }"
      "    }"
      "    node_def {"
      "      name: 'read0'"
      "      op: 'ReadVariableOp'"
      "      input: 'var'"

    TF_SetStatus(status, TF_OK, "");
    PopulateDefaultParam(params);
    params->device_type = "Device0";
  };
  auto plugin_init_1 = [](TP_OptimizerRegistrationParams* const params,
                          TF_Status* const status) -> void {
    TF_SetStatus(status, TF_OK, "");
    PopulateDefaultParam(params);
    params->device_type = "Device1";
  };

  TF_ASSERT_OK(InitGraphPlugin(plugin_init_0));

Variable* Variable::Create(TFE_Context* context, TF_DataType type,
                           const int64_t* dims, const int num_dims,
                           const char* device, TF_Status* status) {
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(
      TFE_NewOp(context, "VarHandleOp", status), TFE_DeleteOp);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op.get(), "dtype", type);