code: def crosstab(
      index,
      columns,
      values=None,
      rownames=None,
      colnames=None,
      aggfunc=None,
      margins=False,
      margins_name="All",
      dropna=None,
      normalize=False,
  ):
      """
      Compute a simple cross tabulation of two (or more) factors. By default
      computes a frequency table of the factors unless an array of values and an
      aggregation function are passed.
  
      Parameters
      ----------
      index : array-like, Series, or list of arrays/Series
          Values to group by in the rows.
      columns : array-like, Series, or list of arrays/Series
          Values to group by in the columns.
      values : array-like, optional
          Array of values to aggregate according to the factors.
          Requires `aggfunc` be specified.
      rownames : list of str, default None
          If passed, must match number of row arrays passed.
      colnames : list of str, default None
          If passed, must match number of column arrays passed.
      aggfunc : function, optional
          If specified, requires `values` be specified as well.
      margins : Not supported
      margins_name : Not supported
      dropna : Not supported
      normalize : Not supported
  
      Returns
      -------
      DataFrame
          Cross tabulation of the data.
  
      Examples
      --------
      >>> a = cudf.Series(["foo", "foo", "foo", "foo", "bar", "bar",
      ...               "bar", "bar", "foo", "foo", "foo"], dtype=object)
      >>> b = cudf.Series(["one", "one", "one", "two", "one", "one",
      ...               "one", "two", "two", "two", "one"], dtype=object)
      >>> c = cudf.Series(["dull", "dull", "shiny", "dull", "dull", "shiny",
      ...               "shiny", "dull", "shiny", "shiny", "shiny"],
      ...              dtype=object)
      >>> cudf.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])
      b   one        two
      c   dull shiny dull shiny
      a
      bar    1     2    1     0
      foo    2     2    1     2
      """
      if normalize is not False:
          raise NotImplementedError("normalize is not supported yet")
  
      if values is None and aggfunc is not None:
          raise ValueError("aggfunc cannot be used without values.")
  
      if values is not None and aggfunc is None:
          raise ValueError("values cannot be used without an aggfunc.")
  
      if not isinstance(index, (list, tuple)):
          index = [index]
      if not isinstance(columns, (list, tuple)):
          columns = [columns]
  
      if not rownames:
          rownames = _get_pivot_names(index, rownames, prefix="row")
      if not colnames:
          colnames = _get_pivot_names(columns, colnames, prefix="col")
  
      if len(index) != len(rownames):
          raise ValueError("index and rownames must have same length")
      if len(columns) != len(colnames):
          raise ValueError("columns and colnames must have same length")
  
      if len(set(rownames)) != len(rownames):
          raise ValueError("rownames must be unique")
      if len(set(colnames)) != len(colnames):
          raise ValueError("colnames must be unique")
  
      data = {
          **dict(zip(rownames, map(as_column, index))),
          **dict(zip(colnames, map(as_column, columns))),
      }
  
      df = cudf.DataFrame._from_data(data)
  
      if values is None:
          df["__dummy__"] = 0
          kwargs = {"aggfunc": "count", "fill_value": 0}
      else:
          df["__dummy__"] = values
          kwargs = {"aggfunc": aggfunc}
  
      table = pivot_table(
          data=df,
          index=rownames,
          columns=colnames,
          values="__dummy__",
          margins=margins,
          margins_name=margins_name,
          dropna=dropna,
          **kwargs,
      )
  
      return table
  
imports: import itertools
  import warnings
  from collections import abc
  from typing import Dict, Optional
  import cupy
  import numpy
  import pandas
  import cudf
  from cudf._lib.transform import one_hot_encode
  from cudf._lib.types import size_type_dtype
  from cudf._typing import Dtype
  from cudf.api.extensions import no_default
  from cudf.core.column import ColumnBase, as_column, column_empty_like
  from cudf.core.column.categorical import CategoricalColumn
  from cudf.utils.dtypes import min_unsigned_type
label_desc: Use image node_id 15 for calling a global function with example usage: crosstab(index, columns, values, rownames, colnames, aggfunc, margins, margins_name, dropna, normalize) and returns: table