我已经做了一个简单的pytorch模型，有3个ANN层，它应该采取一批3通道图像，即torch.size（128，3，32，32）。但是在定义模型之后，当我尝试训练已经传递给gpu的train_dataloader时，它显示AttributeError：“list”对象没有属性“flatten”我该怎么办？代码如下：

class ImageClassificationBase(nn.Module):
    def training_step(self, batch):
        images, labels = batch
        predicted_label = self(images)
        loss = F.cross_entropy(predicted_label, labels)
        return loss
    def validation_step(self, batch):
        images, labels = batch
        predicted_label = self(batch)
        loss = F.cross_entropy(predicted_label, labels)
        acc = accuracy(predicted_label, labels)
        return {'val_loss' : loss.detach(), 'val_acc' : acc}
    def validation_epoch_end(self,val_output):
        batch_losses = [x['val_loss'] for x in val_output]
        epoch_loss = torch.stack(batch_losses).mean()
        batch_acc = [x['val_acc'] for x in val_output]
        epoch_acc = torch.stack(batch_acc).mean()
        return {'val_loss' : epoch_loss.item(), 'val_acc' : epoch_acc.item()}
    def epoch_end(self, epoch, result):
        print(f"Epoch [{epoch}], val_loss : {result['val_loss']:0.4f}, val_acc : {result['val_acc']:0.4f}")
def evaluate(model, val_dataloader):
    outputs = [model.validation_step(batch) for batch in val_dataloader]
    return model.validation_epoch_end(outputs)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def to_device(data, device):
    if isinstance(data, (list, tuple)):
        return [to_device(x, device) for x in data]
    return data.to(device, non_blocking = True)
class DeviceDataLoader():
    def __init__(self, dataloader, device):
        self.dataloader = dataloader
        self.device = device
    def __iter__(self):
        for batch in self.dataloader:
            yield to_device(batch, self.device)
    def __len__(self):
        return len(self.dataloader)
train_loader = DeviceDataLoader(train_dataloader, device)
val_loader = DeviceDataLoader(val_dataloader, device)
test_loader = DeviceDataLoader(test_dataloader, device)
class CIFAR10Model(ImageClassificationBase):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.fc1 = nn.Linear(32 * 32 * 3, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, 10)

    def forward(self, xb):
        x = self.flatten(xb)
        x = self.fc1(x)
        x = torch.relu(x)
        x = self.fc2(x)
        x = torch.relu(x)
        x = self.fc3(x)
        return x
model1 = to_device(CIFAR10Model(), device)
fit(10, 0.01, model, train_loader, val_loader)

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我试着在另一个colab文件上尝试代码，尝试重新启动。但它仍然显示AttributeError：“list”对象没有属性“flatten”