code: def read_csv(
      filepath_or_buffer,
      sep=",",
      delimiter=None,
      header="infer",
      names=None,
      index_col=None,
      usecols=None,
      prefix=None,
      mangle_dupe_cols=True,
      dtype=None,
      true_values=None,
      false_values=None,
      skipinitialspace=False,
      skiprows=0,
      skipfooter=0,
      nrows=None,
      na_values=None,
      keep_default_na=True,
      na_filter=True,
      skip_blank_lines=True,
      parse_dates=None,
      dayfirst=False,
      compression="infer",
      thousands=None,
      decimal=".",
      lineterminator="\n",
      quotechar='"',
      quoting=0,
      doublequote=True,
      comment=None,
      delim_whitespace=False,
      byte_range=None,
      use_python_file_object=True,
      storage_options=None,
      bytes_per_thread=None,
  ):
      """{docstring}"""
  
      if use_python_file_object and bytes_per_thread is not None:
          raise ValueError(
              "bytes_per_thread is only supported when "
              "`use_python_file_object=False`"
          )
  
      if bytes_per_thread is None:
          bytes_per_thread = ioutils._BYTES_PER_THREAD_DEFAULT
  
      is_single_filepath_or_buffer = (
          ioutils.ensure_single_filepath_or_buffer(
              path_or_data=filepath_or_buffer,
              storage_options=storage_options,
          )
      )
      if not is_single_filepath_or_buffer:
          raise NotImplementedError(
              "`read_csv` does not yet support reading multiple files"
          )
  
      (
          filepath_or_buffer,
          compression,
      ) = ioutils.get_reader_filepath_or_buffer(
          path_or_data=filepath_or_buffer,
          compression=compression,
          iotypes=(BytesIO, StringIO, NativeFile),
          use_python_file_object=use_python_file_object,
          storage_options=storage_options,
          bytes_per_thread=bytes_per_thread,
      )
  
      if na_values is not None and is_scalar(na_values):
          na_values = [na_values]
  
      df = libcudf.csv.read_csv(
          filepath_or_buffer,
          lineterminator=lineterminator,
          quotechar=quotechar,
          quoting=quoting,
          doublequote=doublequote,
          header=header,
          mangle_dupe_cols=mangle_dupe_cols,
          usecols=usecols,
          sep=sep,
          delimiter=delimiter,
          delim_whitespace=delim_whitespace,
          skipinitialspace=skipinitialspace,
          names=names,
          dtype=dtype,
          skipfooter=skipfooter,
          skiprows=skiprows,
          dayfirst=dayfirst,
          compression=compression,
          thousands=thousands,
          decimal=decimal,
          true_values=true_values,
          false_values=false_values,
          nrows=nrows,
          byte_range=byte_range,
          skip_blank_lines=skip_blank_lines,
          parse_dates=parse_dates,
          comment=comment,
          na_values=na_values,
          keep_default_na=keep_default_na,
          na_filter=na_filter,
          prefix=prefix,
          index_col=index_col,
      )
  
      if dtype is None or isinstance(dtype, abc.Mapping):
          # There exists some dtypes in the result columns that is inferred.
          # Find them and map them to the default dtypes.
          specified_dtypes = {} if dtype is None else dtype
          df_dtypes = df._dtypes
          unspecified_dtypes = {
              name: df_dtypes[name]
              for name in df._column_names
              if name not in specified_dtypes
          }
          default_dtypes = {}
  
          for name, dt in unspecified_dtypes.items():
              if dt == np.dtype("i1"):
                  # csv reader reads all null column as int8.
                  # The dtype should remain int8.
                  default_dtypes[name] = dt
              else:
                  default_dtypes[name] = _maybe_convert_to_default_type(
                      dt
                  )
          df = df.astype(default_dtypes)
  
      return df
  
file_path: cudf/python/cudf/cudf/io/csv.py
imports: from collections import abc
  from io import BytesIO, StringIO
  import numpy
  from pyarrow.lib import NativeFile
  import cudf
  from cudf import _lib
  from cudf.api.types import is_scalar
  from cudf.utils import ioutils
  from cudf.utils.dtypes import _maybe_convert_to_default_type
  from cudf.utils.nvtx_annotation import _cudf_nvtx_annotate
label_desc: Use image node_id 1 for calling a global function with example usage: read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, prefix, mangle_dupe_cols, dtype, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, skip_blank_lines, parse_dates, dayfirst, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, comment, delim_whitespace, byte_range, use_python_file_object, storage_options, bytes_per_thread) and returns: df