text: import torch
  import torch.nn as nn
  from torchvision import datasets, models, transforms
  
  import torch.nn.functional as F
  
  def conv_block(input_size, output_size):
      block = nn.Sequential(
          nn.Conv2d(input_size, output_size, (3, 3)), nn.ReLU(), nn.BatchNorm2d(output_size), nn.MaxPool2d((2, 2)),
      )
  
      return block
  
  class MyModel(nn.Module):
      def __init__(self,model_cnn,size_out,num_classes):
          
          super(MyModel, self).__init__()
          self.cnn = model_cnn # un model pre entrainé au paravant
          
          self.fc1 = nn.Linear((size_out+1)  , 60) # 512 = btachsize et 20 c'est moi qui ai choisit au pif mdr
          self.fc2 = nn.Linear(60, num_classes)
          
      def forward(self, image, tab):
          x1 = self.cnn(image)
          x2 = tab
  
          x2 =  torch.unsqueeze(x2, 1)
          print(x1.size())
          x = torch.cat((x2, x1), dim=1)
          print(x.size())
          x = F.relu(self.fc1(x))
          x = self.fc2(x)
  
  
          return x
  
  