text: Title: [BUG] Unable to infer shapes for the Q and DQ nodes with INT16 data type.
  Body: # Bug Report
  
  ### Is the issue related to model conversion?
  <!-- If the ONNX checker reports issues with this model then this is most probably related to the converter used to convert the original framework model to ONNX. Please create this bug in the appropriate converter's GitHub repo (pytorch, tensorflow-onnx, sklearn-onnx, keras-onnx, onnxmltools) to get the best help. -->
  No
  ### Describe the bug
  <!-- Please describe the bug clearly and concisely -->
  
  I am using `from onnx.utils import extract_model` to extract a subgraph from a `16bit QDQ` quantized model, but I find that `onnx.shape_inference.infer_shapes` is unable to infer shapes for `16bit QDQ` nodes. It works fine for the other nodes, including `8bit QDQ` nodes.
  
  ### System information
  ONNX version: 1.17.0
  Python version: 3.9.13
  Protobuf version:3.20.3
  
  ### Reproduction instructions
  <!--
  - Describe the code to reproduce the behavior.
  ```
  import onnx
  model = onnx.load('model.onnx')
  ...
  ```
  - Attach the ONNX model to the issue (where applicable)-->
  
  ### Expected behavior
  <!-- A clear and concise description of what you expected to happen. -->
  Since `8bit QDQ` nodes can infer shapes normally, I believe it should also apply to `16bit QDQ` nodes because there won't be any difference in terms of shape.
  ### Notes
  <!-- Any additional information -->
  In addition, I found that using `from onnxruntime.tools.symbolic_shape_infer import SymbolicShapeInference` works fine for `16bit QDQ` nodes. However, I think it might be unreasonable to additionally introduce onnxruntime for `extract_model` in onnx.