code: def load(
      path,
      delimiter=None,
      encoding="utf-8",
      columns_to_keep=None,
      dtype=None,
      header=0,
      names=None,
      format=None,
      nrows=None,
      skiprows=None,
      usecols=None,
      low_memory=False,
      converters=None,
      filters=None,
      sample_count=None,
      worker_count=None,
      multiprocessing_method="forkserver",
  ) -> DataFrame:
      if isinstance(path, list):
          return _load_multipart(
              paths=path,
              delimiter=delimiter,
              encoding=encoding,
              columns_to_keep=columns_to_keep,
              dtype=dtype,
              header=header,
              names=names,
              format=format,
              nrows=nrows,
              skiprows=skiprows,
              usecols=usecols,
              low_memory=low_memory,
              converters=converters,
              filters=filters,
              worker_count=worker_count,
              multiprocessing_method=multiprocessing_method,
          )
      if format is not None:
          pass
      elif path.endswith(save_pointer.POINTER_SUFFIX):
          format = "pointer"
      elif path[-1] == "/" and s3_utils.is_s3_url(
          path
      ):  # and path[:2] == 's3'
          format = "multipart_s3"
      elif path[-1] == "/" and not s3_utils.is_s3_url(
          path
      ):  # and path[:2] != 's3'
          format = "multipart_local"
      elif ".parquet" in path or ".pq" in path or path[-1] == "/":
          format = "parquet"
      else:
          format = "csv"
          if delimiter is None:
              if path.endswith(".tsv"):
                  delimiter = "\t"
                  logger.debug(
                      f"File delimiter for {path} inferred as '\\t' (tab). If this is incorrect, please manually load the data as a pandas DataFrame."
                  )
              else:
                  delimiter = ","
                  logger.debug(
                      f"File delimiter for {path} inferred as ',' (comma). If this is incorrect, please manually load the data as a pandas DataFrame."
                  )
  
      if format == "pointer":
          content_path = load_pointer.get_pointer_content(path)
          return load(
              path=content_path,
              delimiter=delimiter,
              encoding=encoding,
              columns_to_keep=columns_to_keep,
              dtype=dtype,
              header=header,
              names=names,
              format=None,
              nrows=nrows,
              skiprows=skiprows,
              usecols=usecols,
              low_memory=low_memory,
              converters=converters,
              filters=filters,
              sample_count=sample_count,
              worker_count=worker_count,
              multiprocessing_method=multiprocessing_method,
          )
      elif format == "multipart_s3":
          bucket, prefix = s3_utils.s3_path_to_bucket_prefix(path)
          return _load_multipart_s3(
              bucket=bucket,
              prefix=prefix,
              columns_to_keep=columns_to_keep,
              dtype=dtype,
              filters=filters,
              sample_count=sample_count,
              worker_count=worker_count,
              multiprocessing_method=multiprocessing_method,
          )  # TODO: Add arguments!
      elif format == "multipart_local":
          paths = [
              join(path, f)
              for f in listdir(path)
              if (isfile(join(path, f))) & (f.startswith("part-"))
          ]
          return _load_multipart(
              paths=paths,
              delimiter=delimiter,
              encoding=encoding,
              columns_to_keep=columns_to_keep,
              dtype=dtype,
              header=header,
              names=names,
              format=None,
              nrows=nrows,
              skiprows=skiprows,
              usecols=usecols,
              low_memory=low_memory,
              converters=converters,
              filters=filters,
              worker_count=worker_count,
              multiprocessing_method=multiprocessing_method,
          )
      elif format == "parquet":
          try:
              # TODO: Deal with extremely strange issue resulting from torch being present in package,
              #  will cause read_parquet to either freeze or Segmentation Fault when performing multiprocessing
              df = pd.read_parquet(
                  path, columns=columns_to_keep, engine="fastparquet"
              )
          except:
              df = pd.read_parquet(
                  path, columns=columns_to_keep, engine="pyarrow"
              )
          column_count_full = len(df.columns)
      elif format == "csv":
          df = pd.read_csv(
              path,
              converters=converters,
              delimiter=delimiter,
              encoding=encoding,
              header=header,
              names=names,
              dtype=dtype,
              low_memory=low_memory,
              nrows=nrows,
              skiprows=skiprows,
              usecols=usecols,
          )
          column_count_full = len(list(df.columns.values))
          if columns_to_keep is not None:
              df = df[columns_to_keep]
      else:
          raise Exception("file format " + format + " not supported!")
  
      row_count = df.shape[0]
  
      column_count_trimmed = len(list(df.columns.values))
  
      if filters is not None:
          if isinstance(filters, list):
              for filter in filters:
                  df = filter(df)
          else:
              df = filters(df)
  
      logger.log(
          20,
          "Loaded data from: "
          + str(path)
          + " | Columns = "
          + str(column_count_trimmed)
          + " / "
          + str(column_count_full)
          + " | Rows = "
          + str(row_count)
          + " -> "
          + str(len(df)),
      )
      return df
  
file_path: autogluon/common/src/autogluon/common/loaders/load_pd.py
imports: import logging
  import multiprocessing
  from os import listdir
  from os.path import isfile, join
  import pandas
  from pandas import DataFrame
  from ..savers import save_pointer
  from ..utils import multiprocessing_utils, s3_utils
  from .None import load_pointer
  from .load_s3 import list_bucket_prefix_suffix_contains_s3
label_desc: Use image node_id 1 for calling a global function with example usage: load(path, delimiter, encoding, columns_to_keep, dtype, header, names, format, nrows, skiprows, usecols, low_memory, converters, filters, sample_count, worker_count, multiprocessing_method) and returns: df, _load_multipart, load, _load_multipart_s3, _load_multipart