diff --git a/crates/kornia-core-ops/src/ops.rs b/crates/kornia-core-ops/src/ops.rs
index 3fd61bf0..05a2d392 100644
--- a/crates/kornia-core-ops/src/ops.rs
+++ b/crates/kornia-core-ops/src/ops.rs
@@ -3,156 +3,201 @@ use num_traits::Float;
 
 use crate::error::TensorOpsError;
 
-/// Compute the sum of the elements in the tensor along dimension `dim`
-///
-/// # Arguments
-///
-/// * `tensor` - The tensor to sum the elements of.
-/// * `dim` - The index of the dimension/axis to perform the sum operation over.
-///
-/// # Returns
-///
-/// A new `Tensor` containing the element sums.
-///
-/// # Errors
-///
-/// If the requested dimension is greater than the number of axes of the tensor, an error is returned.
-///
-/// # Example
-///
-/// ```
-/// use kornia_core::{Tensor, CpuAllocator};
-/// use kornia_core_ops::ops::sum_elements;
-///
-/// let data: [u8; 6] = [1, 1, 1, 1, 1, 1];
-/// let t = Tensor::<u8, 2>::from_shape_slice([2, 3], &data, CpuAllocator).unwrap();
-/// let agg = sum_elements(&t, 1).unwrap();
-/// assert_eq!(agg.shape, [2, 1]);
-/// assert_eq!(agg.as_slice(), [3, 3]);
-/// ```
-pub fn sum_elements<T, const N: usize, A>(
-    tensor: &Tensor<T, N, A>,
-    dim: usize,
-) -> Result<Tensor<T, N, A>, TensorOpsError>
+/// A trait to define operationt that can be performed on a tensor.
+pub trait TensorOps<T, const N: usize, A>
 where
-    T: SafeTensorType + std::ops::Add<Output = T>,
-    A: TensorAllocator + Clone + 'static,
+    T: SafeTensorType,
+    A: TensorAllocator,
 {
-    if dim >= N {
-        return Err(TensorOpsError::DimOutOfBounds(dim, N - 1));
-    }
-
-    let mut out_shape = tensor.shape;
-    out_shape[dim] = 1;
+    /// Apply the power function to the tensor data.
+    ///
+    /// # Arguments
+    ///
+    /// * `n` - The power to raise the tensor data to.
+    ///
+    /// # Returns
+    ///
+    /// A new tensor with the data raised to the power.
+    fn powf(&self, n: T) -> Tensor<T, N, A>
+    where
+        T: Float;
 
-    let mut out_strides = tensor.strides;
-    if dim > 0 {
-        out_strides
-            .iter_mut()
-            .take(dim)
-            .for_each(|s| *s /= tensor.shape[dim]);
-    }
+    /// Apply the square root function to the tensor elements.
+    ///
+    /// # Returns
+    ///
+    /// A new tensor with the square root values.
+    fn sqrt(&self) -> Tensor<T, N, A>
+    where
+        T: Float;
 
-    let numel: usize = out_shape.iter().product();
-    let mut data = vec![T::default(); numel];
-
-    for (i, v) in tensor.as_slice().iter().enumerate() {
-        let mut out_index = tensor.get_index_unchecked(i);
-        out_index[dim] = 0;
-        let out_offset = out_index
-            .iter()
-            .zip(out_strides.iter())
-            .fold(0, |acc, (&idx, &stride)| acc + idx * stride);
-        let agg = unsafe { data.get_unchecked_mut(out_offset) };
-        *agg = *agg + *v;
-    }
+    /// Compute the sum of the elements in the tensor along dimension `dim`
+    ///
+    /// # Arguments
+    ///
+    /// * `dim` - The index of the dimension/axis to perform the sum operation over.
+    ///
+    /// # Returns
+    ///
+    /// A new `Tensor` containing the element sums.
+    ///
+    /// # Errors
+    ///
+    /// If the requested dimension is greater than the number of axes of the tensor, an error is returned.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use kornia_core::{Tensor, CpuAllocator};
+    /// use kornia_core_ops::ops::TensorOps;
+    ///
+    /// let data: [u8; 6] = [1, 1, 1, 1, 1, 1];
+    /// let t = Tensor::<u8, 2>::from_shape_slice([2, 3], &data, CpuAllocator).unwrap();
+    /// let agg = t.sum_elements(1).unwrap();
+    /// assert_eq!(agg.shape, [2, 1]);
+    /// assert_eq!(agg.as_slice(), [3, 3]);
+    /// ```
+    fn sum_elements(&self, dim: usize) -> Result<Tensor<T, N, A>, TensorOpsError>
+    where
+        T: std::ops::Add<Output = T>;
 
-    let storage = TensorStorage::from_vec(data, tensor.storage.alloc().clone());
+    /// Compute the p-norm of the tensor along some dimension.
+    ///
+    /// # Arguments
+    ///
+    /// * `p` - The order of the norm.
+    /// * `dim` - The index of the dimension/axis to use as vectors for the p-norm calculation.
+    ///
+    /// # Returns
+    ///
+    /// A new `Tensor` containing the p-norm values at all the vector locations.
+    ///
+    /// # Errors
+    ///
+    /// If the requested dimension is greater than the number of axes of the tensor, an error is returned.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use kornia_core::{Tensor, CpuAllocator};
+    /// use kornia_core_ops::ops::TensorOps;
+    ///
+    /// let data: [f32; 5] = [3., 3., 2., 1., 1.];
+    /// let t = Tensor::<f32, 1>::from_shape_slice([5], &data, CpuAllocator).unwrap();
+    /// let norm = t.pnorm(3., 0).unwrap();
+    /// assert_eq!(norm.shape, [1]);
+    /// assert_eq!(norm.as_slice(), [4.]);
+    /// ```
+    fn pnorm(&self, p: T, dim: usize) -> Result<Tensor<T, N, A>, TensorOpsError>
+    where
+        T: std::ops::Add<Output = T> + Float;
 
-    Ok(Tensor {
-        storage,
-        shape: out_shape,
-        strides: out_strides,
-    })
+    /// Compute the euclidean norm of a tensor along some dimension.
+    ///
+    /// # Arguments
+    ///
+    /// * `dim` - The index of the dimension/axis to use as vectors for the norm calculation.
+    ///
+    /// # Returns
+    ///
+    /// A new `Tensor` containing the norm values at all the vector locations.
+    ///
+    /// # Errors
+    ///
+    /// If the requested dimension is greater than the number of axes of the tensor, an error is returned.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use kornia_core::{Tensor, CpuAllocator};
+    /// use kornia_core_ops::ops::TensorOps;
+    ///
+    /// let data: [f32; 5] = [3., 2., 1., 1., 1.];
+    /// let t = Tensor::<f32, 1>::from_shape_slice([5], &data, CpuAllocator).unwrap();
+    /// let norm = t.euclidean_norm(0).unwrap();
+    /// assert_eq!(norm.shape, [1]);
+    /// assert_eq!(norm.as_slice(), [4.]);
+    /// ```
+    fn euclidean_norm(&self, dim: usize) -> Result<Tensor<T, N, A>, TensorOpsError>
+    where
+        T: std::ops::Add<Output = T> + Float;
 }
 
-/// Compute the p-norm of the tensor along some dimension.
-///
-/// # Arguments
-///
-/// * `tensor` - The tensor to calculate the p-norm from.
-/// * `p` - The order of the norm.
-/// * `dim` - The index of the dimension/axis to use as vectors for the p-norm calculation.
-///
-/// # Returns
-///
-/// A new `Tensor` containing the p-norm values at all the vector locations.
-///
-/// # Errors
-///
-/// If the requested dimension is greater than the number of axes of the tensor, an error is returned.
-///
-/// # Example
-///
-/// ```
-/// use kornia_core::{Tensor, CpuAllocator};
-/// use kornia_core_ops::ops::pnorm;
-///
-/// let data: [f32; 5] = [3., 3., 2., 1., 1.];
-/// let t = Tensor::<f32, 1>::from_shape_slice([5], &data, CpuAllocator).unwrap();
-/// let norm = pnorm(&t, 3., 0).unwrap();
-/// assert_eq!(norm.shape, [1]);
-/// assert_eq!(norm.as_slice(), [4.]);
-/// ```
-pub fn pnorm<T, const N: usize, A>(
-    tensor: &Tensor<T, N, A>,
-    p: T,
-    dim: usize,
-) -> Result<Tensor<T, N, A>, TensorOpsError>
+impl<T, const N: usize, A> TensorOps<T, N, A> for Tensor<T, N, A>
 where
-    T: SafeTensorType + std::ops::Add<Output = T> + Float,
-    A: TensorAllocator + Clone + 'static,
+    T: SafeTensorType,
+    A: TensorAllocator + 'static,
 {
-    let p_inv = T::one() / p;
-    Ok(sum_elements(&tensor.powf(p), dim)?.powf(p_inv))
-}
+    fn powf(&self, n: T) -> Tensor<T, N, A>
+    where
+        T: Float,
+    {
+        self.map(|x| x.powf(n))
+    }
 
-/// Compute the euclidean norm of a tensor along some dimension.
-///
-/// # Arguments
-///
-/// * `tensor` - The tensor to calculate the euclidean norm from.
-/// * `dim` - The index of the dimension/axis to use as vectors for the norm calculation.
-///
-/// # Returns
-///
-/// A new `Tensor` containing the norm values at all the vector locations.
-///
-/// # Errors
-///
-/// If the requested dimension is greater than the number of axes of the tensor, an error is returned.
-///
-/// # Example
-///
-/// ```
-/// use kornia_core::{Tensor, CpuAllocator};
-/// use kornia_core_ops::ops::euclidean_norm;
-///
-/// let data: [f32; 5] = [3., 2., 1., 1., 1.];
-/// let t = Tensor::<f32, 1>::from_shape_slice([5], &data, CpuAllocator).unwrap();
-/// let norm = euclidean_norm(&t, 0).unwrap();
-/// assert_eq!(norm.shape, [1]);
-/// assert_eq!(norm.as_slice(), [4.]);
-/// ```
-pub fn euclidean_norm<T, const N: usize, A>(
-    tensor: &Tensor<T, N, A>,
-    dim: usize,
-) -> Result<Tensor<T, N, A>, TensorOpsError>
-where
-    T: SafeTensorType + std::ops::Add<Output = T> + Float,
-    A: TensorAllocator + Clone + 'static,
-{
-    Ok(sum_elements(&tensor.powi(2), dim)?.sqrt())
+    fn sqrt(&self) -> Tensor<T, N, A>
+    where
+        T: Float,
+    {
+        self.map(|x| x.sqrt())
+    }
+
+    fn sum_elements(&self, dim: usize) -> Result<Tensor<T, N, A>, TensorOpsError>
+    where
+        T: std::ops::Add<Output = T>,
+    {
+        if dim >= N {
+            return Err(TensorOpsError::DimOutOfBounds(dim, N - 1));
+        }
+
+        let mut out_shape = self.shape;
+        out_shape[dim] = 1;
+
+        let mut out_strides = self.strides;
+        if dim > 0 {
+            out_strides
+                .iter_mut()
+                .take(dim)
+                .for_each(|s| *s /= self.shape[dim]);
+        }
+
+        let numel: usize = out_shape.iter().product();
+        let mut data = vec![T::default(); numel];
+
+        for (i, v) in self.as_slice().iter().enumerate() {
+            let mut out_index = self.get_index_unchecked(i);
+            out_index[dim] = 0;
+            let out_offset = out_index
+                .iter()
+                .zip(out_strides.iter())
+                .fold(0, |acc, (&idx, &stride)| acc + idx * stride);
+            let agg = unsafe { data.get_unchecked_mut(out_offset) };
+            *agg = *agg + *v;
+        }
+
+        let storage = TensorStorage::from_vec(data, self.storage.alloc().clone());
+
+        Ok(Tensor {
+            storage,
+            shape: out_shape,
+            strides: out_strides,
+        })
+    }
+
+    fn pnorm(&self, p: T, dim: usize) -> Result<Tensor<T, N, A>, TensorOpsError>
+    where
+        T: std::ops::Add<Output = T> + Float,
+    {
+        let p_inv = T::one() / p;
+        Ok(self.powf(p).sum_elements(dim)?.powf(p_inv))
+    }
+
+    fn euclidean_norm(&self, dim: usize) -> Result<Tensor<T, N, A>, TensorOpsError>
+    where
+        T: std::ops::Add<Output = T> + Float,
+    {
+        Ok(self.powi(2).sum_elements(dim)?.sqrt())
+    }
 }
 
 #[cfg(test)]
@@ -165,7 +210,7 @@ mod tests {
     fn test_sum_dim_oob() -> Result<(), TensorError> {
         let data: [u8; 4] = [2, 2, 2, 2];
         let t = Tensor::<u8, 2>::from_shape_slice([2, 2], &data, CpuAllocator)?;
-        let res = sum_elements(&t, 2);
+        let res = t.sum_elements(2);
         assert!(res.is_err_and(|e| e == TensorOpsError::DimOutOfBounds(2, 1)));
         Ok(())
     }
@@ -174,7 +219,7 @@ mod tests {
     fn test_sum_1d() -> Result<(), TensorError> {
         let data: [u8; 4] = [1, 1, 1, 1];
         let t = Tensor::<u8, 1>::from_shape_slice([4], &data, CpuAllocator)?;
-        let res = sum_elements(&t, 0);
+        let res = t.sum_elements(0);
 
         assert!(res.is_ok_and(|v| v.as_slice() == [4]));
         Ok(())
@@ -187,19 +232,19 @@ mod tests {
         let t_f32 = t.cast::<f32>();
         let t_i32 = t.cast::<i32>();
 
-        let agg = sum_elements(&t, 1)?;
+        let agg = t.sum_elements(1)?;
         assert_eq!(agg.shape, [2, 1]);
         assert_eq!(agg.as_slice(), [6, 15]);
 
-        assert_eq!(sum_elements(&t_f32, 1)?.as_slice(), [6., 15.]);
-        assert_eq!(sum_elements(&t_i32, 1)?.as_slice(), [6, 15]);
+        assert_eq!(t_f32.sum_elements(1)?.as_slice(), [6., 15.]);
+        assert_eq!(t_i32.sum_elements(1)?.as_slice(), [6, 15]);
 
-        let agg = sum_elements(&t, 0)?;
+        let agg = t.sum_elements(0)?;
         assert_eq!(agg.shape, [1, 3]);
         assert_eq!(agg.as_slice(), [5, 7, 9]);
 
-        assert_eq!(sum_elements(&t_f32, 0)?.as_slice(), [5., 7., 9.]);
-        assert_eq!(sum_elements(&t_i32, 0)?.as_slice(), [5, 7, 9]);
+        assert_eq!(t_f32.sum_elements(0)?.as_slice(), [5., 7., 9.]);
+        assert_eq!(t_i32.sum_elements(0)?.as_slice(), [5, 7, 9]);
         Ok(())
     }
 
@@ -210,23 +255,23 @@ mod tests {
         let t_f32 = t.cast::<f32>();
         let t_i32 = t.cast::<i32>();
 
-        let agg = sum_elements(&t, 0)?;
+        let agg = t.sum_elements(0)?;
         assert_eq!(agg.shape, [1, 3, 4]);
         assert_eq!(agg.as_slice(), [2; 12]);
-        assert_eq!(sum_elements(&t_f32, 0)?.as_slice(), [2.; 12]);
-        assert_eq!(sum_elements(&t_i32, 0)?.as_slice(), [2; 12]);
+        assert_eq!(t_f32.sum_elements(0)?.as_slice(), [2.; 12]);
+        assert_eq!(t_i32.sum_elements(0)?.as_slice(), [2; 12]);
 
-        let agg = sum_elements(&t, 1)?;
+        let agg = t.sum_elements(1)?;
         assert_eq!(agg.shape, [2, 1, 4]);
         assert_eq!(agg.as_slice(), [3; 8]);
-        assert_eq!(sum_elements(&t_f32, 1)?.as_slice(), [3.; 8]);
-        assert_eq!(sum_elements(&t_i32, 1)?.as_slice(), [3; 8]);
+        assert_eq!(t_f32.sum_elements(1)?.as_slice(), [3.; 8]);
+        assert_eq!(t_i32.sum_elements(1)?.as_slice(), [3; 8]);
 
-        let agg = sum_elements(&t, 2)?;
+        let agg = t.sum_elements(2)?;
         assert_eq!(agg.shape, [2, 3, 1]);
         assert_eq!(agg.as_slice(), [4; 6]);
-        assert_eq!(sum_elements(&t_f32, 2)?.as_slice(), [4.; 6]);
-        assert_eq!(sum_elements(&t_i32, 2)?.as_slice(), [4; 6]);
+        assert_eq!(t_f32.sum_elements(2)?.as_slice(), [4.; 6]);
+        assert_eq!(t_i32.sum_elements(2)?.as_slice(), [4; 6]);
         Ok(())
     }
 
@@ -234,7 +279,7 @@ mod tests {
     fn test_pnorm_1d() -> Result<(), TensorOpsError> {
         let data: [f32; 5] = [3., 3., 2., 1., 1.];
         let t = Tensor::<f32, 1>::from_shape_slice([5], &data, CpuAllocator).unwrap();
-        let norm = pnorm(&t, 3., 0)?;
+        let norm = t.pnorm(3., 0)?;
         assert_eq!(norm.shape, [1]);
         assert_eq!(norm.as_slice(), [4.]);
         Ok(())
@@ -244,7 +289,7 @@ mod tests {
     fn test_euclidean_norm_1d() -> Result<(), TensorOpsError> {
         let data: [f32; 5] = [3., 2., 1., 1., 1.];
         let t = Tensor::<f32, 1>::from_shape_slice([5], &data, CpuAllocator).unwrap();
-        let norm = euclidean_norm(&t, 0)?;
+        let norm = t.euclidean_norm(0)?;
         assert_eq!(norm.shape, [1]);
         assert_eq!(norm.as_slice(), [4.]);
         Ok(())
diff --git a/crates/kornia-core/src/tensor.rs b/crates/kornia-core/src/tensor.rs
index e46ebaf7..3720c01b 100644
--- a/crates/kornia-core/src/tensor.rs
+++ b/crates/kornia-core/src/tensor.rs
@@ -653,36 +653,6 @@ where
         self.map(|x| x.powi(n))
     }
 
-    /// Apply the power function to the tensor data.
-    ///
-    /// # Arguments
-    ///
-    /// * `n` - The power to raise the tensor data to.
-    ///
-    /// # Returns
-    ///
-    /// A new tensor with the data raised to the power.
-    pub fn powf(&self, n: T) -> Tensor<T, N, A>
-    where
-        T: Float,
-        A: Clone,
-    {
-        self.map(|x| x.powf(n))
-    }
-
-    /// Apply the square root function to the tensor elements.
-    ///
-    /// # Returns
-    ///
-    /// A new tensor with the square root values.
-    pub fn sqrt(&self) -> Tensor<T, N, A>
-    where
-        T: Float,
-        A: Clone,
-    {
-        self.map(|x| x.sqrt())
-    }
-
     /// Compute absolute value of the pixel data.
     ///
     /// # Returns