code: def read_dataframe(
      path: str,
      n_rows: int = -1,
      meta_only: bool = False,
      non_missing_columns: Optional[List[str]] = None,
      verbose: bool = False,
      handling: str = "warn",
      fill_columns: Optional[List[str]] = None,
      fill_value: Any = "",
      mode: str = "",
  ) -> pd.DataFrame:
      """Reading a dataframe from different file types
  
      Args:
          path: path of the dataframe
          n_rows: number of rows to limit to
          meta_only: return only meta information
          non_missing_columns: list of columns that cannot contain missing values
          verbose: if warning about dropped rows should be logged
          handling: how to handle missing values
          fill_columns: columns where empty value should be filled (used for empty text)
          fill_value: value to fill empty columns with (used for empty text)
          mode: dataset type, used only for better exception/log information
      Returns:
          dataframe
  
      """
  
      non_missing_columns = (
          [] if non_missing_columns is None else non_missing_columns
      )
      fill_columns = [] if fill_columns is None else fill_columns
  
      meta_info_path = os.path.split(path)
      meta_info_path = os.path.join(
          *meta_info_path[:-1],
          "__meta_info__" + meta_info_path[-1] + ".csv",
      )
      if meta_only and os.path.exists(meta_info_path):
          path = meta_info_path
  
      if path.endswith(".csv"):
          df = pd.read_csv(path, lineterminator="\n").reset_index(
              drop=True
          )
      elif path.endswith(".pq") or path.endswith(".parquet"):
          try:
              df = pd.read_parquet(path, engine="pyarrow").reset_index(
                  drop=True
              )
          except Exception:
              df = pd.read_parquet(
                  path, engine="fastparquet"
              ).reset_index(drop=True)
      elif path.endswith(".json") or path == "":
          return pd.DataFrame()
      else:
          raise ValueError(
              f"Could not determine type of file {path}: "
              f"CSV (`.csv`) and Parquet (`.pq` and `.parquet`) are supported."
          )
  
      if fill_columns:
          df[fill_columns] = df[fill_columns].fillna(fill_value)
  
      if meta_only and os.path.exists(meta_info_path):
          return df
  
      non_missing_columns = [x for x in non_missing_columns if x in df]
      if len(non_missing_columns):
          orig_size = df.shape[0]
          non_missing_index = df[non_missing_columns].dropna().index
          dropped_index = [
              idx for idx in df.index if idx not in non_missing_index
          ]
          df = df.loc[non_missing_index].reset_index(drop=True)
          new_size = df.shape[0]
          if new_size < orig_size and verbose:
              logger.warning(
                  f"Dropped {orig_size - new_size} rows when reading dataframe '{path}' "
                  f"due to missing values encountered in one of the following columns:"
                  f" {non_missing_columns} in the following rows: {dropped_index}"
              )
  
              if handling == "error":
                  dropped_str = dropped_index
  
                  if len(dropped_str) > 10:
                      dropped_str = (
                          dropped_str[:5] + ["..."] + dropped_str[-5:]
                      )
  
                  dropped_str = ", ".join([str(x) for x in dropped_str])
                  prefix = f"{mode} " if mode else ""
                  error = (
                      f"{prefix}dataset contains {len(dropped_index)} rows with missing "
                      f"values in one of the following columns: {non_missing_columns} in "
                      f"the following rows: {dropped_str}"
                  )
  
                  raise ValueError(error.capitalize())
  
      if n_rows > -1:
          df = df.iloc[sample_indices(len(df), n_indices=n_rows)]
  
      # create meta information dataframe if it does not exist
      if not os.path.exists(meta_info_path):
          df_meta = pd.DataFrame(columns=df.columns)
          df_meta.to_csv(meta_info_path, index=False)
  
      return df
  
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 1 for calling a global function with example usage: read_dataframe(path, n_rows, meta_only, non_missing_columns, verbose, handling, fill_columns, fill_value, mode) and returns: df, df, pd