Merge pull request #12 from dantp-ai/solution/task-1-1

Implement approx to derivative of f wrt to arg using central diff approx

minitorch/autodiff.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass
-from typing import Any, Iterable, List, Tuple  # noqa: F401
+from typing import Any, Iterable, Tuple
 
 from typing_extensions import Protocol
 
@@ -22,8 +22,10 @@ def central_difference(f: Any, *vals: Any, arg: int = 0, epsilon: float = 1e-6)
     Returns:
         An approximation of $f'_i(x_0, \ldots, x_{n-1})$
     """
-    # TODO: Implement for Task 1.1.
-    raise NotImplementedError("Need to implement for Task 1.1")
+    return (
+        f(*[v + epsilon if i == arg else v for i, v in enumerate(vals)])
+        - f(*[v - epsilon if i == arg else v for i, v in enumerate(vals)])
+    ) / (2 * epsilon)
 
 
 variable_count = 1