Fix is_active check issue in cavity and dipole (#198)

* Fix `is_active` check issue in cavity and dipole

* Update `CHANGELOG.md`

Author: cr-xu

CHANGELOG.md

@@ -4,7 +4,7 @@
 
 ### 🚨 Breaking Changes
 
-- Cheetah is now vectorised. This means that you can run multiple simulations in parallel by passing a batch of beams and settings, resulting a number of interfaces being changed. For Cheetah developers this means that you now have to account for an arbitrary-dimensional tensor of most of the properties of you element, rather than a single value, vector or whatever else a property was before. (see #116, #157, #170, #172, #173) (@jank324, @cr-xu)
+- Cheetah is now vectorised. This means that you can run multiple simulations in parallel by passing a batch of beams and settings, resulting a number of interfaces being changed. For Cheetah developers this means that you now have to account for an arbitrary-dimensional tensor of most of the properties of you element, rather than a single value, vector or whatever else a property was before. (see #116, #157, #170, #172, #173, #198) (@jank324, @cr-xu)
 
 ### 🚀 Features
 

cheetah/accelerator/cavity.py

@@ -62,7 +62,7 @@ def __init__(
 
     @property
     def is_active(self) -> bool:
-        return any(self.voltage != 0)
+        return torch.any(self.voltage != 0)
 
     @property
     def is_skippable(self) -> bool:

cheetah/accelerator/dipole.py

@@ -99,7 +99,7 @@ def is_skippable(self) -> bool:
 
     @property
     def is_active(self):
-        return any(self.angle != 0)
+        return torch.any(self.angle != 0)
 
     def transfer_map(self, energy: torch.Tensor) -> torch.Tensor:
         device = self.length.device
@@ -108,7 +108,7 @@ def transfer_map(self, energy: torch.Tensor) -> torch.Tensor:
         R_enter = self._transfer_map_enter()
         R_exit = self._transfer_map_exit()
 
-        if any(self.length != 0.0):  # Bending magnet with finite length
+        if torch.any(self.length != 0.0):  # Bending magnet with finite length
             R = base_rmatrix(
                 length=self.length,
                 k1=torch.zeros_like(self.length),