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Add kamada_kawai_layout function (#1583)

* Add kamada_kawai_layout function

Adds rustworkx.kamada_kawai_layout, supporting both PyGraph and
PyDiGraph inputs. Implements the original Kamada-Kawai (1989)
algorithm: an outer loop selects the node with the largest
partial-gradient norm and an inner loop applies a 2D Newton step
against the local Hessian until convergence.

Disconnected graphs are laid out by running Kamada-Kawai
independently on each connected component and packing the
components in a horizontal row, which avoids the visual collapse
seen with single-objective Kamada-Kawai on disconnected inputs.
Directed graphs have their distance matrix symmetrised before
optimising (Kamada-Kawai is fundamentally undirected).

Closes #438

* ruff format: K-K test files

* Remove dead code from kamada_kawai_layout dispatcher

The dispatcher fallthrough should only raise TypeError - the leftover
networkx_converter body was unreachable after the raise and duplicated
the real networkx_converter at line 948.

* Use DictMap for K-K pos parameter

DictMap preserves insertion order matching Python dict semantics. The
fixed parameter remains HashSet since order is never observed (only
contains/is_empty are called on it).

* Reuse edge_weights_from_callable for K-K edge weights

Replaces hand-rolled edge weight extraction with the codebase helper
that already handles weight_fn=None via default_weight, matching the
pattern used by bellman-ford, floyd-warshall, and other shortest-path
functions. Negative-weight validation stays since the helper doesn't
check (and Dijkstra requires non-negative weights).

* Use undirected view for K-K distance matrix

Replaces the min(d_ij, d_ji) symmetrisation with an explicit undirected
graph built from the source topology.  The min-based approach failed
for weakly-connected digraphs: e.g. graph 0->1<-2 yielded d(0,2)=Inf
because neither directed traversal can reach 0 from 2 or vice versa.
Building an undirected view gives d(0,2)... (continued)

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Source Files on job 26096779769.1