od` parameter of some
hypothesis test functions to perform a Monte Carlo version of the
hypothesis tests.

Attributes
----------
n_resamples : int, optional
    The number of Monte Carlo samples to draw. Default is 9999.
batch : int, optional
    The number of Monte Carlo samples to process in each vectorized call to
    the statistic. Batch sizes >>1 tend to be faster when the statistic
    is vectorized, but memory usage scales linearly with the batch size.
    Default is ``None``, which processes all samples in a single batch.
rvs : callable or tuple of callables, optional
    A callable or sequence of callables that generates random variates
    under the null hypothesis. Each element of `rvs` must be a callable
    that accepts keyword argument ``size`` (e.g. ``rvs(size=(m, n))``) and
    returns an N-d array sample of that shape. If `rvs` is a sequence, the
    number of callables in `rvs` must match the number of samples passed
    to the hypothesis test in which the `MonteCarloMethod` is used. Default
    is ``None``, in which case the hypothesis test function chooses values
    to match the standard version of the hypothesis test. For example,
    the null hypothesis of `scipy.stats.pearsonr` is typically that the
    samples are drawn from the standard normal distribution, so
    ``rvs = (rng.normal, rng.normal)`` where
    ``rng = np.random.default_rng()``.
rng : `numpy.random.Generator`, optional
    Pseudorandom number generator state. When `rng` is None, a new
    `numpy.random.Generator` is created using entropy from the
    operating system. Types other than `numpy.random.Generator` are
    passed to `numpy.random.default_rng` to instantiate a ``Generator``.

Nr