/* To figure out the load address we use the definition that for any symbol:
      dynamic_addr(symbol) = static_addr(symbol) + load_addr
 
-     The choice of symbol is arbitrary. The static address we obtain
-     by constructing a non GOT reference to the symbol, the dynamic
-     address of the symbol we compute using adrp/add to compute the
-     symbol's address relative to the PC. */
-
-  ElfW(Addr) static_addr;

  void* dso_handle;
  TF_RETURN_IF_ERROR(env->LoadDynamicLibrary(dso_path.c_str(), &dso_handle));

  // Step 2: Load symbol for `TF_InitPlugin`
  void* dso_symbol;
  TF_RETURN_WITH_CONTEXT_IF_ERROR(
      env->GetSymbolFromLibrary(dso_handle, "TF_InitPlugin", &dso_symbol),
      "Failed to load TF_InitPlugin symbol for DSO: ", dso_path);

  // Step 3: Call `TF_InitPlugin`
  TF_FilesystemPluginInfo info;

}

void* TF_GetSymbolFromLibrary(void* handle, const char* symbol_name,
                              TF_Status* status) {
  void* symbol = nullptr;
  TF_SetStatus(status, TF_OK, "");
  ::tensorflow::Set_TF_Status_from_Status(
      status, ::tensorflow::Env::Default()->GetSymbolFromLibrary(
                  handle, symbol_name, &symbol));
  return symbol;

absl::Status InitGraphPlugin(void* dso_handle) {
  tsl::Env* env = tsl::Env::Default();

  // Step 1: Load symbol for `TF_InitPlugin`
  void* dso_symbol;
  TF_RETURN_IF_ERROR(
      env->GetSymbolFromLibrary(dso_handle, "TF_InitGraph", &dso_symbol));

  // Step 2: Call `TF_InitPlugin`
  auto init_fn = reinterpret_cast<TFInitGraphPluginFn>(dso_symbol);
  return InitGraphPlugin(init_fn);
}

          # metadata. gcr.io helpfully includes it in the header of the response
          # as docker-content-digest: sha256:[digest]. Note we use egrep to
          # match exactly sha256:<hash> because curl may include a ^M symbol at
          # the end of the line.
          # See https://cloud.google.com/architecture/using-container-images#exploring_image_manifests_digests_and_tags
          echo -n "Trying to map name $1 to tag $2... "

# On MacOS, the linker does not support version_script, but has an
# an "-exported_symbols_list" command.  -z defs disallows undefined
# symbols in object files.

tf_cc_shared_object(
    name = "tensorflow",
    linkopts = select({
        "//tensorflow:macos": [
            "-Wl,-exported_symbols_list,$(location //tensorflow/c:exported_symbols.lds)",
        ],
        "//tensorflow:windows": [
        ],

/// but we consider this to be something that users should care about and
/// manage themselves). In both of these cases, core TensorFlow looks for
/// the `TF_InitPlugin` symbol and calls this function.
///
/// For every filesystem URI scheme that this plugin supports, the plugin must
/// add one `TF_FilesystemPluginInfo` entry in `plugin_info->ops` and call

    `tf.distribute.experimental.ParameterServerStrategy`:
    *   Replaces the existing
        `tf.distribute.experimental.ParameterServerStrategy` symbol with a new
        class that is for parameter server training in TF2. Usage of the old
        symbol, usually with Estimator API, should be **replaced** with
        [`tf.compat.v1.distribute.experimental.ParameterServerStrategy`].

from tensorflow.python.platform import app  # pylint: disable=g-import-not-at-top
app.flags = flags

# These symbols appear because we import the python package which
# in turn imports from tensorflow.core and tensorflow.python. They
# must come from this module. So python adds these symbols for the
# resolution to succeed.
# pylint: disable=undefined-variable
del python
del core

      && \
      make -j$(nproc) && \
      make install


# Create the devtoolset libstdc++ linkerscript that links dynamically against
# the system libstdc++ 4.4 and provides all other symbols statically.
# Note that the installation path for libstdc++ here is ${TARGET}/usr/lib64/
mv "${TARGET}/usr/lib64/libstdc++.so.${LIBSTDCXX_VERSION}" \
   "${TARGET}/usr/lib64/libstdc++.so.${LIBSTDCXX_VERSION}.backup"