code: def cartesian(arrays, out=None):
      """Generate a cartesian product of input arrays.
  
      Parameters
      ----------
      arrays : list of array-like
          1-D arrays to form the cartesian product of.
      out : ndarray of shape (M, len(arrays)), default=None
          Array to place the cartesian product in.
  
      Returns
      -------
      out : ndarray of shape (M, len(arrays))
          Array containing the cartesian products formed of input arrays.
          If not provided, the `dtype` of the output array is set to the most
          permissive `dtype` of the input arrays, according to NumPy type
          promotion.
  
          .. versionadded:: 1.2
             Add support for arrays of different types.
  
      Notes
      -----
      This function may not be used on more than 32 arrays
      because the underlying numpy functions do not support it.
  
      Examples
      --------
      >>> from sklearn.utils.extmath import cartesian
      >>> cartesian(([1, 2, 3], [4, 5], [6, 7]))
      array([[1, 4, 6],
             [1, 4, 7],
             [1, 5, 6],
             [1, 5, 7],
             [2, 4, 6],
             [2, 4, 7],
             [2, 5, 6],
             [2, 5, 7],
             [3, 4, 6],
             [3, 4, 7],
             [3, 5, 6],
             [3, 5, 7]])
      """
      arrays = [np.asarray(x) for x in arrays]
      shape = (len(x) for x in arrays)
  
      ix = np.indices(shape)
      ix = ix.reshape(len(arrays), -1).T
  
      if out is None:
          dtype = np.result_type(
              *arrays
          )  # find the most permissive dtype
          out = np.empty_like(ix, dtype=dtype)
  
      for n, arr in enumerate(arrays):
          out[:, n] = arrays[n][ix[:, n]]
  
      return out
  
imports: import warnings
  from functools import partial
  from numbers import Integral
  import numpy
  from scipy import linalg, sparse
  from ..utils import deprecated
  from ..utils._param_validation import Interval, StrOptions, validate_params
  from .None import check_random_state
  from ._array_api import _is_numpy_namespace, device, get_namespace
  from .sparsefuncs_fast import csr_row_norms
  from .validation import check_array
label_desc: Use image node_id 10 for calling a global function with example usage: cartesian(arrays, out) and returns: out