fix: Fix frontend torch.Tensor.unfold method to output correct dimens…

…ions

ivy/functional/frontends/torch/tensor.py

@@ -795,24 +795,35 @@ def new_empty(
         return torch_frontend.tensor(_data)
 
     def unfold(self, dimension, size, step):
+        # Ensure the dimension size is large enough for unfolding
+        if self.shape[dimension] < size:
+            raise ValueError(
+                f"Dimension size ({self.shape[dimension]}) is "
+                "smaller than the unfolding size ({size})."
+            )
+
         slices = []
         self_shape = tuple(self.shape)
+
+        # Create sliding window slices
         for i in range(0, self_shape[dimension] - size + 1, step):
             slicing = [slice(None)] * len(self.shape)
             slicing[dimension] = slice(i, i + size)
             slices.append(self.ivy_array[tuple(slicing)])
-        stacked = torch_frontend.stack(slices, dim=dimension)
+
+        # Stack the slices along a new dimension at 'dimension + 1'
+        stacked = torch_frontend.stack(slices, dim=dimension + 1)
+
+        # Reshape the tensor to insert a new window dimension
         new_shape = list(self.shape)
         num_slices = (self.shape[dimension] - size) // step + 1
+
+        # Replace size of the unfolded dimension with the number of slices
         new_shape[dimension] = num_slices
-        if dimension == -1:
-            new_shape.insert(dimension, size)
-        else:
-            new_shape.insert(dimension + 1, size)
-        reshaped = stacked.reshape(new_shape)
-        dims = list(range(len(stacked.shape)))
-        dims[-2], dims[-1] = dims[-1], dims[-2]
-        return reshaped.permute(*dims)
+
+        # Append the window size at the end (correct behavior)
+        new_shape.append(size)
+        return stacked.reshape(new_shape)
 
     def long(self, memory_format=None):
         self.ivy_array = ivy.astype(self.ivy_array, ivy.int64, copy=False)