undo unneeded change

Author: JacobSzwejbka

exir/tests/control_flow_models.py

@@ -103,66 +103,3 @@ def get_upper_bound_inputs(self):
 
     def get_random_inputs(self):
         return torch.rand(2, 4), torch.rand(4)
-
-
-class FTScanBasic(Module):
-    """Basic scan model that computes cumulative sum."""
-
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, xs: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
-        def combine_fn(carry, x):
-            new_carry = carry + x
-            y = new_carry.clone()
-            return new_carry, y
-
-        init = torch.zeros_like(xs[0])
-        return torch.scan(combine_fn, init, xs)
-
-    def get_random_inputs(self):
-        return (torch.arange(5).float().unsqueeze(1).expand(5, 3),)
-
-
-class FTScanMultipleCarry(Module):
-    """Scan model with multiple carry values (sum and product)."""
-
-    def __init__(self):
-        super().__init__()
-
-    def forward(
-        self, xs: torch.Tensor
-    ) -> tuple[tuple[torch.Tensor, torch.Tensor], torch.Tensor]:
-        def combine_fn(carry, x):
-            sum_carry, prod_carry = carry
-            new_sum = sum_carry + x
-            new_prod = prod_carry * x
-            y = new_sum + new_prod
-            return (new_sum, new_prod), y.clone()
-
-        init_sum = torch.zeros_like(xs[0])
-        init_prod = torch.ones_like(xs[0])
-        return torch.scan(combine_fn, (init_sum, init_prod), xs)
-
-    def get_random_inputs(self):
-        return (torch.arange(1, 5).float().unsqueeze(1).expand(4, 2),)
-
-
-class FTScanWithAdditionalInputs(Module):
-    """Scan model with additional inputs (closure-like behavior)."""
-
-    def __init__(self):
-        super().__init__()
-
-    def forward(
-        self, xs: torch.Tensor, scale: torch.Tensor
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        def combine_fn(carry, x):
-            new_carry = carry + x * scale
-            return new_carry, new_carry.clone()
-
-        init = torch.zeros_like(xs[0])
-        return torch.scan(combine_fn, init, xs)
-
-    def get_random_inputs(self):
-        return (torch.arange(5).float().unsqueeze(1).expand(5, 3), torch.tensor([2.0]))