perf: batch array-valued distribution parameters into a single fill

[vchamarthi]

What

When a distribution parameter is an array rather than a scalar,
vec_cont1_array / vec_cont2_array invoke the fill once per element:

for i from 0 <= i < cnp_PyArray_MultiIter_SIZE(multi):
    func(state, 1, &array_data[i], oa_data[0], ob_data[0])

That is 100,000 VSL calls for a 100,000-element array.

For location and scale families the parameters can be applied after the draw
instead, so one call covers the whole request:

normal(loc, scale)     == loc + scale * N(0, 1)
exponential(scale)     == scale * Exp(1)
lognormal(mean, sigma) == exp(mean + sigma * N(0, 1))

These are exact identities, not approximations.

Routed: normal, uniform, exponential, laplace, gumbel, logistic,
rayleigh, lognormal.

Not routed: gamma, beta, f, chisquare, vonmises, wald,
triangular, pareto, weibull, power, and the discrete distributions.
Their parameters change the shape, so no affine step recovers them. gamma is
kept in the benchmark below as a control.

Numbers

Xeon Gold 6338, single thread, N=100k, ns/element, min of 2 runs. Intel-channel
mkl / mkl-devel 2026.1.0-intel_236, meson build. Baseline and patched
installed in separate conda envs.

distribution	before	after
gumbel	242.6	3.44	70.6x
rayleigh	182.2	2.63	69.4x
exponential	134.3	2.16	62.2x
normal	141.4	3.14	45.1x
lognormal	176.2	4.11	42.8x
laplace	145.4	3.86	37.6x
logistic	71.9	9.85	7.3x
uniform	40.9	8.37	4.9x
gamma (control)	186.4	185.4	1.0x

Scalar-parameter paths are untouched and not measured here.

Streams change

Array-parameter streams change: the draw is now standardised and transformed
rather than parameterised up front. Scalar-parameter paths are byte-identical.
Noted in the changelog.

Testing

• Suite: 190 -> 211 passed, 3 skipped. The 21 new tests also pass on unpatched
  master, so they are invariant guards rather than fitted to this change.
• Constant-valued arrays match the scalar path (assert_allclose).
• KS test against each analytic CDF with per-element varying parameters, D vs
  crit 0.00364: all pass.
• Broadcast shapes, size handling and error cases compared against stock
  numpy: agree, including incompatible size raising ValueError.
• Verified on upstream master 6b7962f.

Notes for reviewers

• _param_out_shape reimplements the shape and compatibility logic rather than
  reusing the multi-iterator, since the batched path has no iterator. It raises
  ValueError("size is not compatible with inputs") where the old path
  surfaced numpy's broadcast error text for some inputs. Same exception type,
  different message.
• lognormal is built on the normal fill, not the lognormal one: its parameters
  sit inside the exponential, so an affine step cannot apply them to a
  standardised lognormal.
• Existing test_uniform_array_bounds_return_ndarray covers this path and still
  passes.