feat: Implement AffinityResult class and optimize camera affinity matrix calculation

- Added a new `AffinityResult` class to encapsulate the results of affinity computations, including the affinity matrix, trackings, and their respective indices.
- Introduced a vectorized implementation of `calculate_camera_affinity_matrix_jax` to enhance performance by leveraging JAX's capabilities, replacing the previous double-for-loop approach.
- Updated tests in `test_affinity.py` to include parameterized benchmarks for comparing the performance of the new vectorized method against the naive implementation, ensuring accuracy and efficiency.

affinity_result.py

@@ -0,0 +1,34 @@
+from dataclasses import dataclass
+from typing import Sequence
+
+from playground import Tracking
+from beartype.typing import Sequence, Mapping
+from jaxtyping import jaxtyped, Float, Int
+from jax import Array
+
+
+@dataclass
+class AffinityResult:
+    """
+    Result of affinity computation between trackings and detections.
+    """
+
+    matrix: Float[Array, "T D"]
+    """
+    Affinity matrix between trackings and detections.
+    """
+
+    trackings: Sequence[Tracking]
+    """
+    Trackings used to compute the affinity matrix.
+    """
+
+    indices_T: Sequence[int]
+    """
+    Indices of the trackings that were used to compute the affinity matrix.
+    """
+
+    indices_D: Sequence[int]
+    """
+    Indices of the detections that were used to compute the affinity matrix.
+    """