feature: remove LinearOperator inheritance from TorchOperator

Author: mrava87

pylops/torchoperator.py

@@ -2,12 +2,12 @@
     "TorchOperator",
 ]
 
-from typing import Optional, Callable
+from typing import Optional
 
 import numpy as np
 
 from pylops import LinearOperator
-from pylops.utils import deps, NDArray
+from pylops.utils import deps
 
 if deps.torch_enabled:
     from pylops._torchoperator import _TorchOperator
@@ -20,7 +20,7 @@
 from pylops.utils.typing import TensorTypeLike
 
 
-class TorchOperator(LinearOperator):
+class TorchOperator:
     """Wrap a PyLops operator into a Torch function.
 
     This class can be used to wrap a pylops operator into a
@@ -63,33 +63,24 @@ def __init__(
             raise NotImplementedError(torch_message)
         self.device = device
         self.devicetorch = devicetorch
-        super().__init__(
-            dtype=np.dtype(Op.dtype), dims=Op.dims, dimsd=Op.dims, name=Op.name
-        )
+        self.dtype = np.dtype(Op.dtype)
+        self.dims, self.dimsd = Op.dims, Op.dimsd
+        self.name = Op.name
         # define transpose indices to bring batch to last dimension before applying
         # pylops forward and adjoint (this will call matmat and rmatmat)
         self.transpf = np.roll(np.arange(2 if flatten else len(self.dims) + 1), -1)
         self.transpb = np.roll(np.arange(2 if flatten else len(self.dims) + 1), 1)
-        self.Op = Op
-        self._register_torchop(batch)
-        self.Top = _TorchOperator.apply
-
-    def _register_torchop(self, batch: bool):
-        # choose _matvec and _rmatvec
-        self.matvec: Callable
-        self.rmatvec: Callable
         if not batch:
-            self.matvec = lambda x: self.Op @ x
-            self.rmatvec = lambda x: self.Op.H @ x
+            self.matvec = lambda x: Op @ x
+            self.rmatvec = lambda x: Op.H @ x
         else:
-            self.matvec = lambda x: (self.Op @ x.transpose(self.transpf)).transpose(self.transpb)
-            self.rmatvec = lambda x: (self.Op.H @ x.transpose(self.transpf)).transpose(self.transpb)
-
-    def _matvec(self, x: NDArray) -> NDArray:
-        return self.matvec(x)
-
-    def _rmatvec(self, x: NDArray) -> NDArray:
-        return self.rmatvec(x)
+            self.matvec = lambda x: (Op @ x.transpose(self.transpf)).transpose(
+                self.transpb
+            )
+            self.rmatvec = lambda x: (Op.H @ x.transpose(self.transpf)).transpose(
+                self.transpb
+            )
+        self.Top = _TorchOperator.apply
 
     def apply(self, x: TensorTypeLike) -> TensorTypeLike:
         """Apply forward pass to input vector