Fixing example

dfdx/examples/advanced-train-loop.rs

@@ -12,7 +12,7 @@ fn classification_train<
     Lbl,
     // Our model just needs to implement these two things! ModuleMut for forward
     // and TensorCollection for optimizer/alloc_grads/zero_grads
-    Model: Module<Inp::Traced, Error = crate::tensor::Error> + ZeroGrads<E, D> + UpdateParams<E, D>,
+    Model: Module<Inp::Traced> + ZeroGrads<E, D> + UpdateParams<E, D>,
     // optimizer, pretty straight forward
     Opt: Optimizer<Model, E, D>,
     // our data will just be any iterator over these items. easy!
@@ -22,7 +22,7 @@ fn classification_train<
     Criterion: FnMut(Model::Output, Lbl) -> Loss,
     // the Loss needs to be able to call backward, and we also use
     // this generic as an output
-    Loss: Backward<E, D, Err = crate::tensor::Error> + AsArray<Array = E>,
+    Loss: Backward<E, D> + AsArray<Array = E>,
     // Dtype & Device to tie everything together
     E: Dtype,
     D: Device<E>,
@@ -32,7 +32,7 @@ fn classification_train<
     mut criterion: Criterion,
     data: Data,
     batch_accum: usize,
-) -> Result<(), crate::tensor::Error> {
+) -> Result<(), Error> {
     let mut grads = model.try_alloc_grads()?;
     for (i, (inp, lbl)) in data.enumerate() {
         let y = model.try_forward_mut(inp.traced(grads))?;