code: def column_slicing_test(col, offset, size, cast_to_float=False):
      col_slice = col.slice(offset, offset + size)
      series = cudf.Series(col)
      sliced_series = cudf.Series(col_slice)
  
      if cast_to_float:
          pd_series = series.astype(float).to_pandas()
          sliced_series = sliced_series.astype(float)
      else:
          pd_series = series.to_pandas()
  
      if cudf.api.types.is_categorical_dtype(col.dtype):
          # The cudf.Series is constructed from an already sliced column, whereas
          # the pandas.Series is constructed from the unsliced series and then
          # sliced, so the indexes should be different and we must ignore it.
          # However, we must compare these as frames, not raw arrays,  because
          # numpy comparison of categorical values won't work.
          assert_eq(
              pd_series[offset : offset + size].reset_index(drop=True),
              sliced_series.reset_index(drop=True),
          )
      else:
          assert_eq(
              np.asarray(pd_series[offset : offset + size]),
              sliced_series.to_numpy(),
          )
  
imports: import cupy
  import numpy
  import pandas
  import pyarrow
  import pytest
  import cudf
  from cudf._lib.transform import mask_to_bools
  from cudf.core.column.column import as_column
  from cudf.testing._utils import assert_eq, assert_exceptions_equal
  from cudf.utils import dtypes
label_desc: Use image node_id 4 for calling a global function with example usage: column_slicing_test(col, offset, size, cast_to_float) without return types