New classification metrics#154
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PiperOrigin-RevId: 952343352
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This PR adds three new core classification accuracy metrics to metrax (BinaryAccuracy, CategoricalAccuracy, and SparseCategoricalAccuraacy) ensuring parity with reference implementations in keras.metrics.
BinaryAccuracy: Calculates frequency of (predictions >= threshold) == labels.
CategoricalAccuracy: Calculates frequency of argmax(predictions, -1) == argmax(labels, -1).
SparseCategoricalAccuracy: Calculates frequency of argmax(predictions, -1) == labels with auto-squeezing for 2D target vectors (batch, 1).
Added parameterized test suites validating exact numerical parity against keras.metrics across float16, float32, and bfloat16 with weighted/unweighted samples and threshold bounds (0.1, 0.5, 0.7)