support onnx export with batch size 1; also works for batch processin…

…g, but the results might be affected by the padding

egs/audioset/AT/zipformer/export-onnx.py

@@ -202,10 +202,7 @@ def forward(
         logits = self.classifier(encoder_out)  # (N, T, num_classes)
         # Note that this is slightly different from model.py for better
         # support of onnx
-        N = logits.shape[0]
-        for i in range(N):
-            logits[i, encoder_out_lens[i] :] = 0
-        logits = logits.sum(dim=1) / encoder_out_lens.unsqueeze(-1)
+        logits = logits.mean(dim=1)
         return logits