code: def _process_col(col, unit, dayfirst, infer_datetime_format, format):
      if col.dtype.kind == "M":
          return col
  
      elif col.dtype.kind in ("f"):
          if unit not in (None, "ns"):
              factor = cudf.Scalar(
                  column.datetime._unit_to_nanoseconds_conversion[unit]
              )
              col = col * factor
  
          if format is not None:
              # Converting to int because,
              # pandas actually creates a datetime column
              # out of float values and then creates an
              # int column out of it to parse against `format`.
              # Instead we directly cast to int and perform
              # parsing against `format`.
              col = (
                  col.astype("int")
                  .astype("str")
                  .as_datetime_column(
                      dtype="datetime64[us]"
                      if "%f" in format
                      else "datetime64[s]",
                      format=format,
                  )
              )
          else:
              col = col.as_datetime_column(dtype="datetime64[ns]")
          return col
  
      elif col.dtype.kind in ("i"):
          if unit in ("D", "h", "m"):
              factor = cudf.Scalar(
                  column.datetime._unit_to_nanoseconds_conversion[unit]
                  / column.datetime._unit_to_nanoseconds_conversion["s"]
              )
              col = col * factor
  
          if format is not None:
              col = col.astype("str").as_datetime_column(
                  dtype=_unit_dtype_map[unit], format=format
              )
          else:
              col = col.as_datetime_column(dtype=_unit_dtype_map[unit])
          return col
  
      elif col.dtype.kind in ("O"):
          if unit not in (None, "ns") or col.null_count == len(col):
              try:
                  col = col.astype(dtype="int64")
              except ValueError:
                  col = col.astype(dtype="float64")
              return _process_col(
                  col=col,
                  unit=unit,
                  dayfirst=dayfirst,
                  infer_datetime_format=infer_datetime_format,
                  format=format,
              )
          else:
              if format is None:
                  if not infer_datetime_format and dayfirst:
                      raise NotImplementedError(
                          f"{dayfirst=} not implemented "
                          f"when {format=} and {infer_datetime_format=}."
                      )
                  format = column.datetime.infer_format(
                      element=col.element_indexing(0),
                      dayfirst=dayfirst,
                  )
              return col.as_datetime_column(
                  dtype=_unit_dtype_map[unit],
                  format=format,
              )
      raise TypeError(
          f"dtype {col.dtype} cannot be converted to {_unit_dtype_map[unit]}"
      )
  
imports: import math
  import re
  import warnings
  from typing import Sequence, Union
  import cupy
  import numpy
  import pandas
  import pandas.tseries.offsets
  from pandas.core.tools.datetimes import _unit_map
  from typing_extensions import Self
  import cudf
  from cudf import _lib
  from cudf._lib.strings.convert.convert_integers import is_integer
  from cudf.api.types import is_integer, is_scalar
  from cudf.core import column
  from cudf.core.index import as_index
label_desc: Use image node_id 2 for calling a global function with example usage: _process_col(col, unit, dayfirst, infer_datetime_format, format) and returns: col, col, col, _process_col, col