[ONNX] Scripted `reshape` incorrect if shape is dynamically calculated
Labels: module: onnx, triaged, onnx-triaged, bug

### 🐛 Describe the bug

torch.onnx.export produces incorrect export of reshape function call after scripting if shape is calculated dynamically. It looks like one of the shape arguments is not converted to integer and is float instead. 

```python
#sample code 

import torch
import torch.nn as nn
import onnx
import onnxruntime

TEST_WINDOW_SIZE = 7
TEST_H = 28
TEST_W = 28
TEST_B = 1
TEST_NUM_WINDOWS = 16
TEST_C = 96


class Model(nn.Module):
    def __init__(self, window_size: int, H: int, W: int):
        
        super(Model, self).__init__()
        
        self.window_size = window_size
        self.H = H
        self.W = W

    def window_reverse(self, windows, window_size: int, H: int, W: int):
        """
        Args:
            windows: (num_windows*B, window_size, window_size, C)
            window_size (int): Window size
            H (int): Height of image
            W (int): Width of image
        Returns:
            x: (B, H, W, C)
        """
        B:int = int(windows.shape[0] / (H * W / window_size / window_size))
        x = windows.reshape(B, H // window_size, W // window_size, window_size, window_size, -1)
        x = x.permute(0, 1, 3, 2, 4, 5)
        x = x.reshape(B, H, W, -1)
        return x

    def forward(self, windows):
        return self.window_reverse(windows, self.window_size, self.H, self.W)


model = Model(TEST_WINDOW_SIZE, TEST_H, TEST_W)
model.eval()
model.cpu()

windows = torch.randn(TEST_NUM_WINDOWS * TEST_B, TEST_WINDOW_SIZE, TEST_WINDOW_SIZE, TEST_C)

jit_model = torch.jit.script(model, example_inputs=[(windows,)])
jit_model.eval()
jit_model.cpu()

torch.testing.assert_allclose(model(windows), jit_model(windows))

torch.onnx.export(jit_model,
    windows,
    "bug.onnx",
    export_params=False,
    opset_version=11,
    do_constant_folding=True,
    input_names=["input"],
    output_names=["output"],
    dynamic_axes={
        'input' : {0 : 'batch_size'}, 
        'output' : {0 : 'batch_size'}
    }, verbose=True)

onnx_model = onnx.load("bug.onnx")
onnx.checker.check_model(onnx_model)

ort_session = onnxruntime.InferenceSession("bug.onnx", verbose=True) 

```

error
```
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Load model from bug.onnx failed:Type Error: Type parameter (T) of Optype (Concat) bound to different types (tensor(float) and tensor(int64) in node (Concat_11).
```



![bug onnx](https://user-images.githubusercontent.com/8251330/171630341-8a82bd63-ffda-4010-85d8-c71d3cd0ede7.png)


### Versions

PyTorch version: 1.11.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04 LTS (x86_64)
GCC version: (Ubuntu 11.2.0-19ubuntu1) 11.2.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.9.12 (main, Apr  5 2022, 06:56:58)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.15.0-25-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.5.119
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 2080 Ti
Nvidia driver version: 495.29.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.2.4
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.22.4
[pip3] torch==1.11.0
[pip3] torchvision==0.12.0
[conda] numpy                     1.22.4                   pypi_0    pypi
[conda] torch                     1.11.0                   pypi_0    pypi
[conda] torchvision               0.12.0                   pypi_0    pypi