diff --git a/pytorch/augmentation/README.md b/pytorch/augmentation/README.md
index 38746e5..eb82234 100644
--- a/pytorch/augmentation/README.md
+++ b/pytorch/augmentation/README.md
@@ -467,7 +467,7 @@ plt.show()
 
 Apply randomly a list of transformations with a given probability.
 
-**Note: It randomly applies the whole list of transformation or none. **
+**Note: It randomly applies the whole list of transformation or none.**
 
 ```python
 randomapply_transform = tv.transforms.RandomApply(
@@ -485,3 +485,41 @@ plt.show()
 ```
 
 ![image25](image25.png)
+
+
+## Building your own filter 
+In the case you need a special filter then you just can write it very easily on your own. Here is an example. 
+
+```python
+import torch
+
+
+class OnOffFilter(torch.nn.Module):
+    def __init__(self, p: float = 0.5) -> None:
+        super(OnOffFilter, self).__init__()
+
+        self.p: float = p
+
+    def forward(self, tensor: torch.Tensor) -> torch.Tensor:
+
+        assert tensor.shape[1] == 1
+
+        tensor -= self.p
+        temp_0: torch.Tensor = torch.where(
+            tensor < 0.0, -tensor, tensor.new_zeros(tensor.shape, dtype=tensor.dtype)
+        )
+        temp_1: torch.Tensor = torch.where(
+            tensor >= 0.0, tensor, tensor.new_zeros(tensor.shape, dtype=tensor.dtype)
+        )
+
+        new_tensor: torch.Tensor = torch.cat((temp_0, temp_1), dim=1)
+
+        return new_tensor
+
+    def __repr__(self):
+        return self.__class__.__name__ + "(p={0})".format(self.p)
+
+
+if __name__ == "__main__":
+    pass
+```