- Include code to invoke the TFLite Converter Python API and the errors.
- 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.

      self.fail(
          "Failed to import"
          f" {len(failed_packages)}/{len(self.api_packages_v2)} packages"
          f" {version}:\n{failed_packages}"
      )
    logging.info("Import of packages was successful.")


if __name__ == "__main__":

// Flush local buffers to the filesystem. If the process terminates after a
// successful flush, the contents may still be persisted, since the underlying
// filesystem may eventually flush the contents.  If the OS or machine crashes
// after a successful flush, the contents may or may not be persisted, depending
// on the implementation.

  ///
  /// 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
  /// re-fetch the block if the state is ERROR.
  enum class FetchState {
    CREATED,
    FETCHING,
    FINISHED,

        lra_list_.push_front(key);
        block->lra_iterator = lra_list_.begin();
        block->timestamp = timer_seconds_();
      }
    }
  });
  // Loop until either block content is successfully fetched, or our request
  // encounters an error.
  absl::MutexLock l(&block->mu);
  TF_SetStatus(status, TF_OK, "");
  while (true) {
    switch (block->state) {
      case FetchState::ERROR:

    TF_SetStatus(status, TF_UNKNOWN, "Could not get content-length header");
    return -1;
  }
  // `TF_OUT_OF_RANGE` isn't considered as an error. So we clear it here.
  TF_SetStatus(status, TF_OK, "");
  TF_VLog(1, "Successful read of %s @ %u of size: %u", path.c_str(), offset,
          read);
  stream.read(buffer, read);
  read = stream.gcount();
  if (read < buffer_size) {

  ///
  /// On successful deletion, `status` must be `TF_OK` and `*undeleted_files`
  /// and `*undeleted_dirs` must be 0. On unsuccessful deletion, `status` must
  /// be set to the reason why one entry couldn't be removed and the proper
  /// count must be updated. If the deletion is unsuccessful because the
  /// traversal couldn't start, `*undeleted_files` must be set to 0 and

// TF_GraphNumFunctions(g). In this case, all the functions registered in
// `g` will be returned. Else, an unspecified subset.
//
// If successful, returns the number of TF_Function* successfully set in
// `funcs` and sets status to OK. The caller takes ownership of
// all the returned TF_Functions. They must be deleted with TF_DeleteFunction.

      params.ninputs <= 0 || params.cond_inputs == nullptr ||
      params.body_inputs == nullptr || params.body_outputs == nullptr) {
    s->status = InvalidArgument(
        "TF_WhileParams must be created by successful TF_NewWhile() call");
    return false;
  }
  return true;
}

bool ValidateInputWhileParams(const TF_WhileParams& params, TF_Status* s) {
  if (params.cond_output.oper == nullptr) {

# Move the updated file to the appropriate directory
mv "$REQUIREMENTS_LOCK_FILE" ../wheel_test/