class CardiacDetectionModel(pl.LightningModule):
def init(self):
super().init()
    self.model = torchvision.models.resnet18(pretrained=True)
    self.model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    self.model.fc = torch.nn.Linear(in_features=512 ,out_features=4)
    
    self.optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-4)
    self.loss_fn = torch.nn.MSELoss()
    
def forward(self, data):
    return self.model(data)

def training_step(self, batch, batch_idx):
    x_ray, label = batch
    label = label.float()
    pred = self(x_ray)
    loss = self.loss_fn(pred, label)
    
    self.log("Train Loss", loss)
    
    if batch_idx % 50 == 0:
        self.log_images(x_ray.cpu(), pred.cpu(), label.cpu(), "Train")
    return loss

def validation_step(self, batch, batch_idx):
    x_ray, label = batch
    label = label.float()
    pred = self(x_ray)
    loss = self.loss_fn(pred, label)
    
    self.log("Val Loss", loss)
    
    if batch_idx % 50 == 0:
        self.log_images(x_ray.cpu(), pred.cpu(), label.cpu(), "Val")
    return loss

def log_images(self, x_ray, pred, label, name):
    results = []
    
    for i in range(4):
        coords_labels = label[i]
        coords_pred = pred[i]
        
        img = ((x_ray[i] * 0.252)+0.494).numpy()[0]
        
        x0, y0 = coords_labels[0].int().item(), coords_labels[1].int().item()
        x1, y1 = coords_labels[2].int().item(), coords_labels[3].int().item()
        img = cv2.rectangle(img, (x0, y0), (x1, y1), (0, 0, 0), 2)
        
        x0, y0 = coords_pred[0].int().item(), coords_pred[1].int().item()
        x1, y1 = coords_pred[2].int().item(), coords_pred[3].int().item()
        img = cv2.rectangle(img, (x0, y0), (x1, y1), (1, 1, 1), 2)
        
        results.append(torch.tensor(img).unsqueeze(0))
    
    grid = torchvision.utils.make_grid(results, 2)
    self.logger.experiment.add_image(name, grid, self.global_step)
    
def configure_optimizers(self):
    #注意してください ここでは必ずリストを返す必要があります（オプティマイザーを1つにまとめるだけです :)

    return [self.optimizer]

モデルオブジェクトの作成
model = CardiacDetectionModel() # モデルをインスタンシエートする

When instantiating the model, if I use google colab, there is no error, but if I use kaggle’s kernel, there is an error.