code: def russellrao(u, v, w=None):
      """
      Compute the Russell-Rao dissimilarity between two boolean 1-D arrays.
  
      The Russell-Rao dissimilarity between two boolean 1-D arrays, `u` and
      `v`, is defined as
  
      .. math::
  
        \\frac{n - c_{TT}}
             {n}
  
      where :math:`c_{ij}` is the number of occurrences of
      :math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for
      :math:`k < n`.
  
      Parameters
      ----------
      u : (N,) array_like, bool
          Input array.
      v : (N,) array_like, bool
          Input array.
      w : (N,) array_like, optional
          The weights for each value in `u` and `v`. Default is None,
          which gives each value a weight of 1.0
  
      Returns
      -------
      russellrao : double
          The Russell-Rao dissimilarity between vectors `u` and `v`.
  
      Examples
      --------
      >>> from scipy.spatial import distance
      >>> distance.russellrao([1, 0, 0], [0, 1, 0])
      1.0
      >>> distance.russellrao([1, 0, 0], [1, 1, 0])
      0.6666666666666666
      >>> distance.russellrao([1, 0, 0], [2, 0, 0])
      0.3333333333333333
  
      """
      u = _validate_vector(u)
      v = _validate_vector(v)
      if u.dtype == v.dtype == bool and w is None:
          ntt = (u & v).sum()
          n = float(len(u))
      elif w is None:
          ntt = (u * v).sum()
          n = float(len(u))
      else:
          w = _validate_weights(w)
          ntt = (u * v * w).sum()
          n = w.sum()
      return float(n - ntt) / n
  
imports: import math
  import warnings
  import numpy
  import dataclasses
  from typing import Optional, Callable
  from functools import partial
  from scipy._lib._util import _asarray_validated
  from .None import _distance_wrap
  from .None import _hausdorff
  from ..linalg import norm
  from ..special import rel_entr
  from .None import _distance_pybind
label_desc: Use image node_id 35 for calling a global function with example usage: russellrao(u, v, w) and returns: unknown