code: def is_valid_data_frame(path: str, csv_rows: int = 100) -> bool:
      """Checking data frame format
  
      Args:
          path: path of the dataframe
          csv_rows: number of rows to limit to when checking csv files
  
      Returns:
          bool
  
      """
      try:
          if path.endswith(".csv"):
              pd.read_csv(path, nrows=csv_rows, lineterminator="\n")
          elif path.endswith(".pq") or path.endswith(".parquet"):
              pq.ParquetFile(path)
          else:
              raise ValueError(
                  f"Could not determine type of file {path}: "
                  f"CSV (`.csv`) and Parquet (`.pq` and `.parquet`) are supported."
              )
      except Exception as e:
          logger.error(str(e))
          return False
      return True
  
imports: import logging
  import math
  import os
  from typing import Any, DefaultDict, Dict, List, Optional, Tuple, Union, no_type_check
  import networkx
  import numpy
  import pandas
  import pyarrow.parquet
  import torch
  from sklearn.model_selection import GroupShuffleSplit, train_test_split
  from torch import distributed
  from torch.utils.data import DataLoader, Sampler, SequentialSampler
  from llm_studio.src.datasets.conversation_chain_handler import ConversationChainHandler
  from llm_studio.src.utils.exceptions import LLMDataException
  from llm_studio.src.utils.utils import PatchedAttribute, set_seed
label_desc: Use image node_id 4 for calling a global function with example usage: is_valid_data_frame(path, csv_rows) and returns: True, False