a function definition to make that definition weak.  */
@@ -125,14 +131,16 @@
    If weak aliases are not available, this defines a strong alias.  */
 # define weak_alias(name, aliasname) _weak_alias (name, aliasname)
 # define _weak_alias(name, aliasname) \
-  extern __typeof (name) aliasname __attribute__ ((weak, alias (#name)));
+  extern __typeof (name) aliasname __attribute__ ((weak, alias (#name))) \
+    __attribute_copy__ (name);
 

## Attribution

# ==============================================================================

name: Upload SIG Build docker containers regularly

on:
  workflow_dispatch:
  schedule:
      # Run once a week on Sunday at midnight. See http://crontab.guru
      - cron: '0 0 * * 0'
  push:
    paths:
      - '.github/workflows/sigbuild-docker.yml'
      - 'tensorflow/tools/tf_sig_build_dockerfiles/**'

#else
#define TF_CAPI_EXPORT __declspec(dllimport)
#endif  // TF_COMPILE_LIBRARY
#else
#ifdef TF_CAPI_WEAK
#define TF_CAPI_EXPORT \
  __attribute__((visibility("default"))) __attribute((weak))
#else
#define TF_CAPI_EXPORT __attribute__((visibility("default")))
#endif  // TF_CAPI_WEAK
#endif  // _WIN32
#endif  // SWIG

// TF_Bool is the C API typedef for unsigned char, while TF_BOOL is

  CHECK(v2_reader_ != nullptr);
  CHECK(v2_reader_->status().ok());

  // First pass: filters out the entries of the slices.
  std::unordered_set<string> filtered_keys;
  BundleEntryProto entry;
  v2_reader_->Seek(kHeaderEntryKey);
  for (v2_reader_->Next(); v2_reader_->Valid(); v2_reader_->Next()) {
    CHECK(entry.ParseFromArray(v2_reader_->value().data(),
                               v2_reader_->value().size()))

-   You make the change and submit it for the review again.
-   This cycle repeats itself until the PR gets approved.
-   Note: As a friendly reminder, we may reach out to you if the PR is awaiting
    your response for more than 2 weeks.

**4. Approved**

-   Once the PR is approved, it gets `kokoro:force-run` label applied and it
    initiates CI/CD tests.
-   We can't move forward if these tests fail.

            to manually call `Model._set_inputs` when using Custom Training
            Loops(CTLs).
        *   Dynamic shapes are supported for generators by calling the Model on
            the first batch we "peek" from the generator. This used to happen
            implicitly in `Model._standardize_user_data`. Long-term, a solution
            where the `DataAdapter` doesn't need to call the Model is probably
            preferable.