)

config_setting(
    name = "linux_aarch64",
    values = {"cpu": "aarch64"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "linux_armhf",
    values = {"cpu": "armhf"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "linux_x86_64",
    values = {"cpu": "k8"},
    visibility = ["//visibility:public"],
)

config_setting(

          ./tensorflow/tools/ci_build/ci_build.sh cpu.arm64 bash tensorflow/tools/ci_build/rel/ubuntu/cpu_arm64_test.sh...

  if ((tensorflow::IsGoogleCudaEnabled() || tensorflow::IsBuiltWithROCm()) &&
      num_virtual_gpus > 0) {
    tensorflow::GPUOptions* gpu_options =
        server_def.mutable_default_session_config()->mutable_gpu_options();
    auto virtual_devices =
        gpu_options->mutable_experimental()->add_virtual_devices();
    for (int i = 0; i < num_virtual_gpus; ++i) {
      virtual_devices->add_memory_limit_mb(200);
    }
  }

            "//tensorflow/c:tf_buffer_internal",
            "//tensorflow/c:tf_status_internal",
            "//tensorflow/c:tf_tensor_internal",
            "//tensorflow/core:core_cpu",
            "//tensorflow/core:core_cpu_internal",
            "//tensorflow/core:framework",
            "//tensorflow/core:framework_internal",
            "//tensorflow/core:lib",
            "//tensorflow/core:lib_internal",

  // Find and return a function record added by its name.
  virtual core::RefCountPtr<FunctionRecord> FindRecord(
      const string& name) const = 0;

  // Return the ParsedName of Host CPU device.
  virtual const DeviceNameUtils::ParsedName& HostCPUParsedName() const = 0;
  virtual const string& HostCPUName() const = 0;

  // Configure soft device placement policy.

        TF_NewOperation(host_graph_, func_name_, func_node_name_);
    for (auto input : inputs) {
      TF_AddInput(desc, input);
    }
    // Set device to CPU because some ops inside the function might not be
    // available on GPU.
    TF_SetDevice(desc, "/cpu:0");
    *op = TF_FinishOperation(desc, s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
    ASSERT_NE(*op, nullptr);
  }

  FunctionDef fdef() {

#endif  // !IS_MOBILE_PLATFORM

using tensorflow::string;

namespace {

string DeviceName(const tensorflow::Device* d) {
  return (d == nullptr) ? "cpu:0" : d->name();
}

// Annotate eager runtime construction context to the given `function_def` as
// an attribute.
void AnnotateEagerRuntimeConstructionContext(
    tensorflow::FunctionDef& function_def) {

  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  std::vector<std::string> devices{
      "/job:localhost/replica:0/task:0/device:CPU:0",
      "/job:localhost/replica:0/task:0/device:CPU:1"};
  ParallelDevice parallel_device(std::move(devices));
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> handle_op(
      TFE_NewOp(context.get(), "VarHandleOp", status.get()), TFE_DeleteOp);

  Args:
    environ_cp: copy of the os.environ.
    var_name: string for name of environment variable, e.g. "TF_NEED_CUDA".
    query_item: string for feature related to the variable, e.g. "CUDA for
      Nvidia GPUs".
    enabled_by_default: boolean for default behavior.
    question: optional string for how to ask for user input.
    yes_reply: optional string for reply when feature is enabled.

  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  const char remote_device_name[] =
      "/job:localhost/replica:0/task:1/device:CPU:0";
  const char local_device_name[] =
      "/job:localhost/replica:0/task:0/device:CPU:0";
  CheckRemoteMatMulExecutesOK(ctx, remote_device_name, local_device_name);

  TFE_Executor* executor = TFE_ContextGetExecutorForThread(ctx);