[QNN-EP] Introduce Level1 Transformer into qnn.preprocess (microsoft#25883)

### Description
- Introduce Level1 Transformer into qnn.preprocess to support various optimizations.

### Motivation and Context
- This change brings in several useful optimizations such as `ConvBnFusion` and `ConstantFolding`, which are part of
`TransformerLevel::Level1` and can benefit QNNEP.
- The goal is to optimize the ONNX model before quantization by integrating these passes into the Python tooling workflow.

Author: qti-hungjuiw

onnxruntime/python/tools/quantization/execution_providers/qnn/preprocess.py

@@ -6,15 +6,15 @@
 from __future__ import annotations
 
 import logging
+import tempfile
 from pathlib import Path
 
 import onnx
 
-from ....tools.onnx_model_utils import fix_output_shapes, make_input_shape_fixed
+from ....tools.onnx_model_utils import fix_output_shapes, make_input_shape_fixed, optimize_model
 from ....tools.remove_initializer_from_input import remove_initializer_from_input
 from ...fusions import FusionGelu, FusionLayerNormalization
 from ...onnx_model import ONNXModel
-from ...quant_utils import save_and_reload_model_with_shape_infer
 from .fusion_lpnorm import FusionLpNormalization
 from .fusion_spacetodepth import FusionSpaceToDepth
 
@@ -93,7 +93,7 @@ def qnn_preprocess_model(
     """
     modified = False
     model = model_input if isinstance(model_input, onnx.ModelProto) else onnx.load_model(model_input)
-    model = save_and_reload_model_with_shape_infer(model)
+    model = save_and_reload_optimize_model(model, shape_infer=True)
     onnx_model = ONNXModel(model)
 
     # Optionally, fix the dynamic input shapes.
@@ -178,6 +178,24 @@ def qnn_preprocess_model(
     return modified
 
 
+def save_and_reload_optimize_model(model: onnx.ModelProto, shape_infer: bool) -> onnx.ModelProto:
+    with tempfile.TemporaryDirectory(prefix="ort.qnn_preproc.") as qnn_preproc_tmp_dir:
+        model_in_path = Path(qnn_preproc_tmp_dir).joinpath("qnn_proc_input.onnx")
+        onnx.save_model(model, model_in_path, save_as_external_data=True)
+        if shape_infer:
+            model_infer_path = Path(qnn_preproc_tmp_dir).joinpath("qnn_proc_infer.onnx")
+            onnx.shape_inference.infer_shapes_path(str(model_in_path), str(model_infer_path))
+            model_in_path = model_infer_path
+        model_out_path = Path(qnn_preproc_tmp_dir).joinpath("qnn_proc_output.onnx")
+        optimize_model(model_in_path, model_out_path)
+        ret_model = onnx.load_model(model_out_path)
+        ret_metaprops = {"onnx.infer": "onnxruntime.tools.qnn.preprocess"}
+        if ret_model.metadata_props:
+            ret_metaprops.update(ret_model.metadata_props)
+        onnx.helper.set_model_props(ret_model, ret_metaprops)
+        return ret_model
+
+
 class InputOutputNameMap:
     def __init__(
         self,