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()))

## 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/**'

//
// This is intended to be used when a peer failure is detected.
TF_CAPI_EXPORT extern void TFE_AbortCollectiveOps(TFE_Context* ctx,
                                                  TF_Status* status);

// Checks the health of collective ops peers. Explicit health check is needed in
// multi worker collective ops to detect failures in the cluster.  If a peer is
// down, collective ops may hang.

            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.