I have resolve the issues by rewriting the code to as follows:

# Load and preprocess the data
qb = QuantBook()
spy = qb.AddEquity("SPY")
history = qb.History(qb.Securities.Keys, datetime(2020, 1, 1), datetime(2023, 1, 1), Resolution.Daily)
# Select the close column and then call the unstack method.
close = history['close'].unstack(level=0)
# Initialize MinMaxScaler to scale the data onto [0,1].
scaler = MinMaxScaler(feature_range=(0, 1))
df = pd.DataFrame(scaler.fit_transform(close), index=close.index)
# Select input data
input_ = df.iloc[1:]
# Shift the data for 1-step backward as training output result.
output = df.shift(-1).iloc[:-1]
# Split the data into training and testing sets.
splitter = int(input_.shape[0] * 0.8)
X_train = input_.iloc[:splitter]
X_test = input_.iloc[splitter:]
y_train = output.iloc[:splitter]
y_test = output.iloc[splitter:]
# training
train_features = []
train_labels = []
for i in range(10, X_train.shape[0]):
   train_features.append(X_train.iloc[i-10:i].values.reshape(-1, 1))
   train_labels.append(y_train.iloc[i])
train_features, train_labels = np.array(train_features), np.array(train_labels)
train_features = np.reshape(train_features, (train_features.shape[0], train_features.shape[1], 1))
# testing
test_features = []
test_labels = []
for i in range(10, X_test.shape[0]):
   test_features.append(X_test.iloc[i-10:i].values.reshape(-1, 1))
   test_labels.append(y_test.iloc[i])
test_features, test_labels = np.array(test_features), np.array(test_labels)
test_features = np.reshape(test_features, (test_features.shape[0], test_features.shape[1], 1))
# Convert the data to PyTorch tensors
X_train = torch.from_numpy(train_features).float()
y_train = torch.from_numpy(train_labels).float()
X_test = torch.from_numpy(test_features).float()
y_test = torch.from_numpy(test_labels).float()
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