code: def read_dataframe_drop_missing_labels(
      path: str, cfg: Any
  ) -> pd.DataFrame:
      if isinstance(cfg.dataset.prompt_column, tuple):
          input_cols = list(cfg.dataset.prompt_column)
      else:
          input_cols = [cfg.dataset.prompt_column]
      verbose = cfg.environment._local_rank == 0
      fill_columns = get_fill_columns(cfg)
      df = read_dataframe(
          path,
          non_missing_columns=input_cols,
          verbose=verbose,
          fill_columns=fill_columns,
      )
      df[input_cols] = df[input_cols].fillna("").astype(str)
      if (
          hasattr(cfg.dataset, "answer_column")
          and cfg.dataset.answer_column in df.columns
      ):
          df[cfg.dataset.answer_column] = (
              df[cfg.dataset.answer_column].fillna("").astype(str)
          )
      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 3 for calling a global function with example usage: read_dataframe_drop_missing_labels(path, cfg) and returns: df