code: def _generic_complete(
      df: pd.DataFrame, columns: list, all_scalars: bool, sort: bool
  ):
      """Generate cartesian product for `_computations_complete`.
  
      Returns a DataFrame, with no duplicates.
      """
      if all_scalars:
          uniques = {
              col: pd.factorize(df[col], sort=sort)[-1]
              for col in columns
          }
          uniques = _computations_expand_grid(uniques)
          uniques = pd.DataFrame(uniques, copy=False)
          uniques.columns = columns
          return uniques
  
      uniques = {}
      for ind, column in enumerate(columns):
          if isinstance(column, dict):
              len_columns = len(columns)
              column = _complete_column(column, df=df, sort=sort)
              # iteration here avoids any potential index collision
              column = {
                  ind + len_columns + key: value
                  for key, value in column.items()
              }
              uniques.update(column)
          else:
              uniques[ind] = _complete_column(column, df=df, sort=sort)
      uniques = _computations_expand_grid(uniques)
      uniques = pd.DataFrame(uniques, copy=False)
      uniques.columns = uniques.columns.droplevel(0)
      return uniques
  
file_path: pyjanitor/janitor/functions/complete.py
imports: import functools
  from typing import Any, Dict, List, Optional, Tuple, Union
  import pandas
  import pandas_flavor
  from pandas.api.types import is_list_like, is_scalar
  from pandas.core.common import apply_if_callable
  from janitor.functions.utils import _computations_expand_grid
  from janitor.utils import check, check_column
label_desc: Use image node_id 3 for calling a global function with example usage: _generic_complete(df, columns, all_scalars, sort) and returns: uniques, uniques