Repository: opencv
Issue: Import ONNX model error DNN module
Labels: bug,category: dnn (onnx)

### System Information

// example for python user
OpenCV C++ version: 3.4.15
Operating System / Platform: Windows 10
Python version: 3.9.10
Pytorch Version: 2.0.1


### Detailed description

I am trying to import a pytorch Vision Transformer model for image classification using `cv::dnn::readNetFromONNX` function.

I am getting below error: 
```DNN/ONNX: ERROR during processing node with 2 inputs and 1 outputs: [Mul]:(onnx::Equal_175)```

Vision Transformer Model: 
[https://huggingface.co/timm/vit_small_patch16_224.augreg_in21k](url)

My .pth and .onnx model transfer learned for specific usecase (12 Classes):
[https://drive.google.com/drive/folders/1MePM7WZEzNqXFxMPPQ2Rm2f1DJ2en8ws?usp=sharing](https://drive.google.com/drive/folders/1MePM7WZEzNqXFxMPPQ2Rm2f1DJ2en8ws?usp=sharing)


### Steps to reproduce

```cpp

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.utils.data import SubsetRandomSampler
from torchvision.models import resnet50
from torchvision.models import vgg16
from torchsummary import summary
from torchvision import datasets, models, transforms
from torch.optim import lr_scheduler
import time
import os
import copy
import torchvision
import timm
from PIL import Image
import torch.onnx 
import torchvision.transforms as transforms
from torch.autograd import Variable

# Let's build our model 
# Load a pre-trained Vision Transformer model from timm
model = timm.create_model('vit_small_patch16_224', pretrained=True
)
# Modify the last fully connected layer for our task
model.head = nn.Linear(model.head.in_features, 12)

# Load the state dictionary
state_dict = torch.load("pth_path", map_location=torch.device('cpu'))

# Load the state dictionary into the model
model.load_state_dict(state_dict)

# Conversion to ONNX 
Convert_ONNX()


#Function to Convert to ONNX 
def Convert_ONNX(): 

    model.eval() 
    # Let's create a dummy input tensor  
    dummy_input= Variable(torch.randn(1, 3, 224, 224))

    
    inp_image = Image.open("sample_image_path")

    preprocess =  transform = transforms.Compose([
        transforms.Resize((224, 224)),  # Resize images to 224x224
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  # Normalize the image tensors
    ])

    input_tensor = preprocess(inp_image)

    inp_batch = input_tensor.unsqueeze(0)

    # Export the model   
    torch.onnx.export(model,         # model being run 
         dummy_input,       # model input (or a tuple for multiple inputs) 
         "ImageClassifier2.onnx",       # where to save the model  
        export_params=True,  # store the trained parameter weights inside the model file 
        opset_version=12)   # the ONNX version to export the model to 
    
    print(" ") 
    print('Model has been converted to ONNX')
```


### Issue submission checklist

- [X] I report the issue, it's not a question
- [X] I checked the problem with documentation, FAQ, open issues, forum.opencv.org, Stack Overflow, etc and have not found any solution
- [x] I updated to the latest OpenCV version and the issue is still there
- [X] There is reproducer code and related data files (videos, images, onnx, etc)