code: def crosstab(
      index,
      columns,
      values=None,
      rownames=None,
      colnames=None,
      aggfunc=None,
      margins: bool = False,
      margins_name: Hashable = "All",
      dropna: bool = True,
      normalize: bool
      | Literal[0, 1, "all", "index", "columns"] = False,
  ) -> DataFrame:
      """
      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 : sequence, default None
          If passed, must match number of row arrays passed.
      colnames : sequence, default None
          If passed, must match number of column arrays passed.
      aggfunc : function, optional
          If specified, requires `values` be specified as well.
      margins : bool, default False
          Add row/column margins (subtotals).
      margins_name : str, default 'All'
          Name of the row/column that will contain the totals
          when margins is True.
      dropna : bool, default True
          Do not include columns whose entries are all NaN.
      normalize : bool, {'all', 'index', 'columns'}, or {0,1}, default False
          Normalize by dividing all values by the sum of values.
  
          - If passed 'all' or `True`, will normalize over all values.
          - If passed 'index' will normalize over each row.
          - If passed 'columns' will normalize over each column.
          - If margins is `True`, will also normalize margin values.
  
      Returns
      -------
      DataFrame
          Cross tabulation of the data.
  
      See Also
      --------
      DataFrame.pivot : Reshape data based on column values.
      pivot_table : Create a pivot table as a DataFrame.
  
      Notes
      -----
      Any Series passed will have their name attributes used unless row or column
      names for the cross-tabulation are specified.
  
      Any input passed containing Categorical data will have **all** of its
      categories included in the cross-tabulation, even if the actual data does
      not contain any instances of a particular category.
  
      In the event that there aren't overlapping indexes an empty DataFrame will
      be returned.
  
      Reference :ref:`the user guide <reshaping.crosstabulations>` for more examples.
  
      Examples
      --------
      >>> a = np.array(["foo", "foo", "foo", "foo", "bar", "bar",
      ...               "bar", "bar", "foo", "foo", "foo"], dtype=object)
      >>> b = np.array(["one", "one", "one", "two", "one", "one",
      ...               "one", "two", "two", "two", "one"], dtype=object)
      >>> c = np.array(["dull", "dull", "shiny", "dull", "dull", "shiny",
      ...               "shiny", "dull", "shiny", "shiny", "shiny"],
      ...              dtype=object)
      >>> pd.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
  
      Here 'c' and 'f' are not represented in the data and will not be
      shown in the output because dropna is True by default. Set
      dropna=False to preserve categories with no data.
  
      >>> foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])
      >>> bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])
      >>> pd.crosstab(foo, bar)
      col_0  d  e
      row_0
      a      1  0
      b      0  1
      >>> pd.crosstab(foo, bar, dropna=False)
      col_0  d  e  f
      row_0
      a      1  0  0
      b      0  1  0
      c      0  0  0
      """
      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 is_nested_list_like(index):
          index = [index]
      if not is_nested_list_like(columns):
          columns = [columns]
  
      common_idx = None
      pass_objs = [
          x
          for x in index + columns
          if isinstance(x, (ABCSeries, ABCDataFrame))
      ]
      if pass_objs:
          common_idx = get_objs_combined_axis(
              pass_objs, intersect=True, sort=False
          )
  
      rownames = _get_names(index, rownames, prefix="row")
      colnames = _get_names(columns, colnames, prefix="col")
  
      # duplicate names mapped to unique names for pivot op
      (
          rownames_mapper,
          unique_rownames,
          colnames_mapper,
          unique_colnames,
      ) = _build_names_mapper(rownames, colnames)
  
      from pandas import DataFrame
  
      data = {
          **dict(zip(unique_rownames, index)),
          **dict(zip(unique_colnames, columns)),
      }
      df = DataFrame(data, index=common_idx)
  
      if values is None:
          df["__dummy__"] = 0
          kwargs = {"aggfunc": len, "fill_value": 0}
      else:
          df["__dummy__"] = values
          kwargs = {"aggfunc": aggfunc}
  
      # error: Argument 7 to "pivot_table" of "DataFrame" has incompatible type
      # "**Dict[str, object]"; expected "Union[...]"
      table = df.pivot_table(
          "__dummy__",
          index=unique_rownames,
          columns=unique_colnames,
          margins=margins,
          margins_name=margins_name,
          dropna=dropna,
          observed=False,
          **kwargs,  # type: ignore[arg-type]
      )
  
      # Post-process
      if normalize is not False:
          table = _normalize(
              table,
              normalize=normalize,
              margins=margins,
              margins_name=margins_name,
          )
  
      table = table.rename_axis(index=rownames_mapper, axis=0)
      table = table.rename_axis(columns=colnames_mapper, axis=1)
  
      return table
  
imports: from __future__ import annotations
  from collections.abc import Hashable, Sequence
  from typing import TYPE_CHECKING, Callable, Literal, cast
  import warnings
  import numpy
  from pandas._libs import lib
  from pandas.util._decorators import Appender, Substitution
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.cast import maybe_downcast_to_dtype
  from pandas.core.dtypes.common import is_list_like, is_nested_list_like, is_scalar
  from pandas.core.dtypes.dtypes import ExtensionDtype
  from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
  import pandas.core.common
  from pandas.core.frame import _shared_docs
  from pandas.core.groupby import Grouper
  from pandas.core.indexes.api import Index, MultiIndex, get_objs_combined_axis
  from pandas.core.reshape.concat import concat
  from pandas.core.reshape.util import cartesian_product
  from pandas.core.series import Series
label_desc: Use image node_id 9 for calling a global function with example usage: crosstab(index, columns, values, rownames, colnames, aggfunc, margins, margins_name, dropna, normalize) and returns: table