var_to_data_type_map_.reset(new TensorSliceReader::VarToDataTypeMap(
        reader_->GetVariableToDataTypeMap()));
  }
}

bool CheckpointReader::HasTensor(const string& name) const {
  if (reader_ != nullptr) {
    return reader_->HasTensor(name, nullptr, nullptr);
  }
  return v2_reader_->Contains(name);
}

const TensorSliceReader::VarToShapeMap&
CheckpointReader::GetVariableToShapeMap() const {

//
// The class currently only interacts with single-slice (i.e., non-partitioned)
// variables.
class CheckpointReader {
 public:
  CheckpointReader(const string& filename, TF_Status* status);

  bool HasTensor(const string& name) const;
  const string DebugString() const;

  // Returns a map from variable names to their shapes.  Slices of a partitioned
  // tensor are combined into a single entry.

}

void TF_DeleteCheckpointReader(TF_CheckpointReader* reader) { delete reader; }

int TF_CheckpointReaderHasTensor(TF_CheckpointReader* reader,
                                 const char* name) {
  return reader->HasTensor(name);
}

const char* TF_CheckpointReaderGetVariable(TF_CheckpointReader* reader,
                                           int index) {
  return reader->variable_list[index].c_str();
}

        affect code that is relying on certain internal details:
    *   Code that uses `isinstance(x, tf.Tensor)` instead of `tf.is_tensor` when
        checking Keras symbolic inputs/outputs should switch to using
        `tf.is_tensor`.
    *   Code that is overly dependent on the exact names attached to symbolic
        tensors (e.g. assumes there will be ":0" at the end of the inputs,