release(v2.0.0-alpha.2): PyO3 bindings + executed companion notebooks + Sphinx RST with inline plots

PyO3 abi3 bindings for the 13 v2.0.0 functions across 8 module groups:
  bsde, pde, stochastic_control, optimal_control::quadratic_impact_control,
  mean_field::mckean_vlasov, agent_based, inference, optimization.

8 executed companion notebooks under examples/notebooks/10_bsde.ipynb …
17_generative_calibration.ipynb (cell outputs and matplotlib figures
preserved as proof-of-work; verified against analytic ground truths).

8 Sphinx RST pages under docs/source/algorithms/{bsde,pde,stochastic_control,
quadratic_impact_control,mckean_vlasov,agent_based,robust_drift,
generative_calibration_hooks}.rst with .. math:: derivations and inline
.. image:: directives placed immediately after each .. code-block:: python
so each plot appears directly under the code that produced it.

18 PNG plot assets under docs/source/_static/v2/<group>/.

index.rst extended with a new 'v2.0 Generic Stochastic Control & PDE'
toctree caption.

Forbidden-vocabulary audit on new src/, docs/source/algorithms/ and
binding files: zero matches.

All previously stable APIs untouched; v2.0.0 is additive at the binding
level — no v1.x function signature was changed.

docs/source/algorithms/generative_calibration_hooks.rst

@@ -0,0 +1,66 @@
+Generative calibration — Gaussian MMD loss
+==========================================
+
+Maximum-Mean-Discrepancy distance with Gaussian kernel (`mmd_gaussian`).  Self-distance is exactly zero; the metric grows monotonically with sample shift.
+
+.. note:: Companion executed notebook: `17_generative_calibration.ipynb <../../examples/notebooks/17_generative_calibration.ipynb>`_
+
+17 — MMD calibration loss
+=========================
+
+.. code-block:: python
+
+   import numpy as np
+   import matplotlib.pyplot as plt
+   from optimizr import _core as opt
+   plt.rcParams['figure.figsize'] = (7, 4)
+   plt.rcParams['figure.dpi'] = 110
+
+.. code-block:: python
+
+   x = np.linspace(0.0, 5.0, 80)
+   shifts = np.linspace(0.0, 6.0, 40)
+   d = [opt.mmd_gaussian(x.tolist(), (x + s).tolist(), 1.0) for s in shifts]
+   print('MMD self =', d[0])
+   print('MMD at shift 6.0 =', d[-1])
+
+.. code-block:: python
+
+   fig, ax = plt.subplots()
+   ax.plot(shifts, d, lw=2)
+   ax.set_xlabel('translation Δ'); ax.set_ylabel('MMD(P, P + Δ)')
+   ax.set_title('Gaussian-kernel MMD vs translation (σ = 1)')
+   ax.grid(alpha=0.3); fig.tight_layout(); plt.show()
+
+.. image:: ../_static/v2/generative_calibration_hooks/plot_01.png
+   :align: center
+   :width: 80%
+
+Bandwidth dependence
+--------------------
+
+.. code-block:: python
+
+   fig, ax = plt.subplots()
+   for sigma in [0.25, 0.5, 1.0, 2.0]:
+       d = [opt.mmd_gaussian(x.tolist(), (x + s).tolist(), sigma) for s in shifts]
+       ax.plot(shifts, d, label=f'σ = {sigma:g}')
+   ax.set_xlabel('translation Δ'); ax.set_ylabel('MMD'); ax.legend(); ax.grid(alpha=0.3)
+   ax.set_title('MMD as a function of kernel bandwidth')
+   fig.tight_layout(); plt.show()
+
+.. image:: ../_static/v2/generative_calibration_hooks/plot_02.png
+   :align: center
+   :width: 80%
+
+**Verified:** `MMD(x, x) = 0`; metric is strictly monotonic in shift.
+
+API
+---
+
+.. code-block:: rust
+
+   pub fn mmd_distance(x: &[f64], y: &[f64], loss: &MmdLoss) -> Result<f64>;
+   pub fn calibration_step<S: GenerativeSampler>(sampler: &mut S, target: &[f64], loss: &MmdLoss, lr: f64) -> Result<f64>;
+   pub trait GenerativeSampler { fn sample(&self, n: usize, seed: u64) -> Vec<f64>; fn parameters(&self) -> Vec<f64>; fn perturb(&mut self, deltas: &[f64]); }
+   pub struct MmdLoss { pub sigma: f64 }