apt-get update
apt-get -y build-dep patchelf/jammy
apt-get -b source patchelf/jammy

### Typical Pull Request Workflow -

**1. New PR**

- As a contributor, you submit a New PR on GitHub.
- We inspect every incoming PR and add certain labels to the PR such as `size:`,
  `comp:` etc.  At this stage we check if the PR is valid and meets certain
  quality requirements. For example, we check if the CLA is signed, PR has
  sufficient description, if applicable unit tests are added, if it is a

  typedef std::pair<std::string, size_t> Key;

  /// \brief The state of a block.
  ///
  /// A block begins in the CREATED stage. The first thread will attempt to read
  /// the block from the filesystem, transitioning the state of the block to
  /// FETCHING. After completing, if the read was successful the state should
  /// be FINISHED. Otherwise the state should be ERROR. A subsequent read can

          downloaded_block = true;
          block->state = FetchState::FINISHED;
        } else {
          block->state = FetchState::ERROR;
        }
        block->cond_var.SignalAll();
        return;
      case FetchState::FETCHING:
        block->cond_var.WaitWithTimeout(&block->mu, absl::Minutes(1));
        if (block->state == FetchState::FINISHED) {
          return TF_SetStatus(status, TF_OK, "");

  TFE_DeleteContext(ctx);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);
}
BENCHMARK(BM_InitOp);

void BM_Execute(::testing::benchmark::State& state) {
  const int async = state.range(0);
  state.SetLabel(async ? "ExecuteAsync" : "Execute");
  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();

  int32_t init_timeout_in_ms = 300000;
  TFE_Context* ctx_0 =
      CreateContext(serialized_server_def_0,
                    /*isolate_session_state=*/false, init_timeout_in_ms);
  TFE_Context* ctx_1 =
      CreateContext(serialized_server_def_1,
                    /*isolate_session_state=*/false, init_timeout_in_ms);

  // Remote device on `worker2`.
  const char remote_device[] = "/job:localhost/replica:0/task:2/device:CPU:0";

  // Advance the fake timestamp so that "a" becomes stale via its first block.
  env->SetNowSeconds(now + 2);
  // The pruning thread periodically compares env->NowSeconds() with the oldest
  // block's timestamp to see if it should evict any files. At the current fake
  // timestamp of `now` + 2, file "a" is stale because its first block is stale,
  // but file "b" is not stale yet. Thus, once the pruning thread wakes up (in

std::string ModularFileSystemTest::cloud_path_;
std::string ModularFileSystemTest::tmp_dir_;

// As some of the implementations might be missing, the tests should still pass
// if the returned `Status` signals the unimplemented state.
bool UnimplementedOrReturnsCode(Status actual_status, Code expected_code) {
  Code actual_code = actual_status.code();
  return (actual_code == Code::UNIMPLEMENTED) || (actual_code == expected_code);
}

  // When `reset_context` is true, initialize new cluster context state based
  // on cluster configurations provided in `server_def`; otherwise, update
  // existing context state with the provided `server_def`. Contexts created
  // on remote tasks will be considered stale and garbage collected after
  // `keep_alive_secs` of inactivity.

- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```

### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:

- Model produces wrong results and/or has lesser accuracy.
- Model produces correct results, but it is slower than expected.

### 4. (optional) RNN conversion support