code: def read(self, path: Path):
      try:
          import parquet
      except ImportError as e:
          raise ImportError(
              f"{e}. HINT: You can install Parquet by running "
              "'pip install parquet'"
          )
  
      with path.open("rb") as tf:
          columns = [
              self.lhs_col,
              self.rhs_col,
              self.rel_col,
              self.weight_col,
          ]
          fetch_columns = [c for c in columns if c is not None]
          for row in parquet.reader(tf, columns=fetch_columns):
              offset = 0
              ret = []
              for c in columns:
                  if c is not None:
                      ret.append(row[offset])
                      offset += 1
                  else:
                      ret.append(None)
  
              yield tuple(ret)
  
imports: import datetime
  import random
  from abc import ABC, abstractmethod
  from contextlib import ExitStack
  from pathlib import Path
  from typing import Any, Counter, Dict, Iterable, List, Optional, Tuple
  import torch
  from torchbiggraph.config import EntitySchema, RelationSchema
  from torchbiggraph.converters.dictionary import Dictionary
  from torchbiggraph.edgelist import EdgeList
  from torchbiggraph.entitylist import EntityList
  from torchbiggraph.graph_storages import AbstractEdgeAppender, AbstractEdgeStorage, AbstractEntityStorage, AbstractRelationTypeStorage, EDGE_STORAGES, ENTITY_STORAGES, RELATION_TYPE_STORAGES
  from torchbiggraph.types import UNPARTITIONED
label_desc: Use image node_id 2 for calling the ParquetEdgelistReader obj's underlying member method code with example usage: obj.read(path) without return types