ValueError: not enough values to unpack (expected 2, got 1) when training model

Question

I am trying a model using pytorch but i am getting this error ValueError: not enough values to unpack (expected 2, got 1).

This is how my data looks

data = np.random.rand(400, 46, 55, 46)

# Datasets initialization
ds = TensorDataset(torch.from_numpy(data))
train_ds, valid_ds = random_split(ds, (350, 50))

# Dataloader wrappers
train_dl, valid_dl = DataLoader(train_ds), DataLoader(valid_ds)

model

class AutoEncoder(pl.LightningModule):
  def __init__(self):
    super(AutoEncoder, self).__init__()
    self.encoder = nn.Sequential(
        nn.Linear(46*55*46, 400),
        nn.Tanh())
    self.decoder = nn.Sequential(
        nn.Linear(400, 46*55*46),
        nn.Sigmoid())
    
  def forward(self, x):
    x = self.encoder(x)
    x = self.decoder(x)
    return x

  def configure_optimizers(self):
    optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
    return optimizer

  def training_step(self, train_batch, batch_idx):
    x,y = train_batch
    x = x.view(x.size(0), -1)
    z = self.encoder(x)
    x_hat = self.decoder(z)
    loss = F.mse_loss(x_hat, x)
    self.log('train_loss', loss)
    return loss

  def validation_step(self, val_batch, batch_idx):
    x, y = val_batch
    x = x.view(x.size(0), -1)
    z = self.encoder(x)
    x_hat = self.decoder(z)
    loss = F.mse_loss(x_hat, x)
    self.log('val_loss', loss)   
model = AutoEncoder()