A language model generates a three-step reasoning path to solve a problem. A separate classification model evaluates each step, assigning a probability to potential labels. The final classification for a step, denoted as $C(\mathbf{x}, \bar{\mathbf{y}}_{\leq k})$, is the label with the highest probability. The overall score for the path is calculated by summing up the number of steps classified as 'correct', based on the formula: $r(\mathbf{x}, \mathbf{y}) = \sum_{k=1}^{n_s} \delta(correct, C(\mathbf{x}, \bar{\mathbf{y}}_{\leq k}))$, where $\delta$ is 1 if its two arguments are identical and 0 otherwise. Given the classifier's probability outputs below, what is the final score for the entire reasoning path?