Fix bias shape corruption in optimizers with proper workflow

CRITICAL FIXES:
- Fixed Adam & SGD optimizers corrupting parameter shapes with variable batch sizes
- Root cause: param.data = Tensor() created new tensor with wrong shape
- Solution: Use param.data._data[:] = ... to preserve original shape

CLAUDE.md UPDATES:
- Added CRITICAL RULE: Never modify core files directly
- Established mandatory workflow: Edit source → Export → Test
- Clear consequences for violations to prevent source/compiled mismatch

TECHNICAL DETAILS:
- Source fix in modules/source/10_optimizers/optimizers_dev.py
- Temporary fix in tinytorch/core/optimizers.py (needs proper export)
- Preserves parameter shapes across all batch sizes
- Enables variable batch size training without broadcasting errors

VALIDATION:
- Created comprehensive test suite validating shape preservation
- All optimizer tests pass with arbitrary batch sizes
- Ready for CIFAR-10 training with variable batches

modules/source/10_optimizers/optimizers_dev.py

@@ -795,10 +795,9 @@ class Adam:
                 )
                 
                 # Update parameter with adaptive learning rate
-                param.data = Tensor(
-                    param.data.data - self.learning_rate * first_moment_corrected / 
-                    (np.sqrt(second_moment_corrected) + self.epsilon)
-                )
+                # CRITICAL: Preserve original parameter shape - don't create new Tensor
+                update = self.learning_rate * first_moment_corrected / (np.sqrt(second_moment_corrected) + self.epsilon)
+                param.data.data = param.data.data - update
         ### END SOLUTION
     
     def zero_grad(self) -> None: