istance between them.

n_jobs : int, default=None
    The number of jobs to use for the computation. This works by
    breaking down the pairwise matrix into n_jobs even slices and
    computing them in parallel.

    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
    ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
    for more details.

working_memory : float, default=None
    The sought maximum memory for temporary distance matrix chunks.
    When None (default), the value of
    ``sklearn.get_config()['working_memory']`` is used.

**kwds : optional keyword parameters
    Any further parameters are passed directly to the distance function.
    If using a scipy.spatial.distance metric, the parameters are still
    metric dependent. See the scipy docs for usage examples.

Yields
------
D_chunk : {ndarray, sparse matrix}
    A contiguous slice of distance matrix, optionally processed by
    ``reduce_func``.

Examples
--------
Without reduce_func:

>>> import numpy as np
>>> from sklearn.metrics import pairwise_distances_chunked
>>> X = np.random.RandomState(0).rand(5, 3)
>>> D_chunk = next(pairwise_distances_chunked(X))
>>> D_chunk
array([[0.  ..., 0.29..., 0.41..., 0.19..., 0.57...],
       [0.29..., 0.  ..., 0.57..., 0.41..., 0.76...],
       [0.41..., 0.57..., 0.  ..., 0.44..., 0.90...],
       [0.19..., 0.41..., 0.44..., 0.  ..., 0.51...],
       [0.57..., 0.76..., 0.90..., 0.51..., 0.  ...]])

Retrieve all neighbors and average distance within radius r:

>>> r = .2
>>> def reduce_func(D_chunk, start):
...     neigh = [np.flatnonzero(d < r) for d in D_chunk]
...     avg_dist = (D_chunk * (D_chunk < r)).mean(axis=1)
...     return neigh, avg_dist
>>> gen = pairwise_distances_chunked(X, reduce_func=reduce_func)
>>> neigh, avg_dist = next(gen)
>>> neigh
[array([0, 3]), array([1]), array([2]), array([0, 3]), array([4])]
>>> avg_dist
array([0.039..., 0.        , 0.        , 0.039..., 0.        ])

Where r is defined per sample, we need to make use of ``start``:

>>> r = [.2, .4, .4, .3, .1]
>>> def reduce_func(D_chunk, start):
...     neigh = [np.flatnonzero(d < r[i])
...              for i, d in enumerate(D_chunk, start)]
...     return neigh
>>> neigh = next(pairwise_distances_chunked(X, reduce_func=reduce_func))
>>> neigh
[array([0, 3]), array([0, 1]), array([2]), array([0, 3]), array([4])]

Force row-by-row generation by reducing ``working_memory``:

>>> gen = pairwise_distances_chunked(X, reduce_func=reduce_func,
...                                  working_memory=0)
>>> next(gen)
[array([0, 3])]
>>> next(gen)
[array([0, 1])]
r@