Fix Mixtral Parity test to keep it consistent with Transformers. (mic…

…rosoft#20210)

### Description
I recently opened a PR in hf transformers repo to fix an issue on the
indexing part.

huggingface/transformers#29857

onnx exporter was failing because of the tolist() conversion so we had
to remove it.

I found out that the code was also a part of our codebase so this PR is
to keep the code consistent.

onnxruntime/test/python/transformers/test_parity_mixtral_moe.py

@@ -293,15 +293,11 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             if top_x.shape[0] == 0:
                 continue
 
-            # in torch it is faster to index using lists than torch tensors
-            top_x_list = top_x.tolist()
-            idx_list = idx.tolist()
-
             # Index the correct hidden states and compute the expert hidden state for
             # the current expert. We need to make sure to multiply the output hidden
             # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
-            current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
-            current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
+            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+            current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
 
             # However `index_add_` only support torch tensors for indexing so we'll use
             # the `top_x` tensor here.