Fixed small bug in embedding.py

src/graphnet/models/components/embedding.py

@@ -120,10 +120,11 @@ def forward(
         seq_length: Tensor,
     ) -> Tensor:
         """Forward pass."""
-        if max(self.mapping)+1 > x.shape[2]:
+        mapping_max = max(i for i in self.mapping if i is not None)+1
+        if mapping_max > x.shape[2]:
             raise IndexError(f"Fourier mapping does not fit given data."
                              f"Feature space of data is too small (size {x.shape[2]})," 
-                             f"given fourier mapping requires at least {max(self.mapping) + 1}.")
+                             f"given fourier mapping requires at least {mapping_max}.")
 
         length = torch.log10(seq_length.to(dtype=x.dtype))
         embeddings = [self.sin_emb(4096 * x[:, :, self.mapping[:3]]).flatten(-2)]  # Position