InvalidArgumentError: Expected multiples argument to be a vector of length 1 but got length 2 (LSTM)

I am trying to develop a LSTM model which can predict a target variable from predictors. Here is my LSTM code:

import pandas as pd
from pandas import DataFrame
from pandas import concat
from pandas import read_csv
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
import keras
import keras.backend as K
from keras.models import Sequential
from keras.layers import Dense, SimpleRNN, Dropout, LSTM
from keras.layers import Activation
from keras.utils import plot_model, np_utils
from keras.regularizers import L1L2
from hyperas import optim
#from hyperas.distributions import choice, uniform, conditional
from hyperas.distributions import choice, uniform
from hyperopt import Trials, STATUS_OK, tpe
from math import sqrt
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import random as rn
import os
matplotlib.rcParams.update({'font.size': 8})

#functions
def series_to_supervised(data, n_in=1,  
                         n_out=1,
                         dropnan=True):
    n_vars = 1 if type(data) is list else data.shape[1]
    df = DataFrame(data)
    cols, names = list(), list()
    # input sequence (t-n, ... t-1)
    for i in range(n_in, 0, -1):
        cols.append(df.shift(i))
        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
    # forecast sequence (t, t+1, ... t+n)
    for i in range(0, n_out):
        cols.append(df.shift(-i))
        if i == 0:
            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
        else:
            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
    # put it all together
    agg = concat(cols, axis=1)
    agg.columns = names
    # drop rows with NaN values
    if dropnan:
        agg.dropna(inplace=True)
    return agg

def rmse(y_true, y_pred):
    return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))

def mse(y_true, y_pred):
    return K.mean(K.square(y_pred - y_true), axis=-1)

def mae(y_true, y_pred):
    return K.mean(K.abs(y_pred - y_true), axis=-1)

def data():
    def series_to_supervised(data, n_in=1, 
                             n_out=1, 
                             dropnan=True):
        n_vars = 1 if type(data) is list else data.shape[1]
        df = DataFrame(data)
        cols, names = list(), list()
        for i in range(n_in, 0, -1):
            cols.append(df.shift(i))
            names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)]
        for i in range(0, n_out):
            cols.append(df.shift(-i))
            if i == 0:
                names += [('var%d(t)' % (j + 1)) for j in range(n_vars)]
            else:
                names += [('var%d(t+%d)' % (j + 1, i)) for j in range(n_vars)]
        agg = concat(cols, axis=1)
        agg.columns = names
        if dropnan:
            agg.dropna(inplace=True)
        return agg

    n_lags = 12
    n_train = 360
    n_test = 60
    
    dataset_raw = read_csv("D:/Binata/bin_ann_lstm_trial/data_Q/NCEP_1984_2018_1.csv",
                           index_col=None, parse_dates=True, infer_datetime_format=True)
    
    train_dates = dataset_raw[['Datetime', 'Q', 'pr']].iloc[:n_train]
    test_dates = dataset_raw[['Datetime', 'Q', 'pr']].iloc[n_train:]
    
    test_dates = test_dates.reset_index(drop=True)
    test_dates['Datetime'] = pd.to_datetime(test_dates['Datetime'])
    
    dataset = dataset_raw.drop(dataset_raw.columns[[0]], axis=1)
    values = dataset.values  # to make just values
    values = values.astype('float32')
    
    Q = values[:, 0]  # to make just values
    Q = Q.reshape(Q.shape[0], 1)
    pr = values[:, 2]  # to make just values
    pr = pr.reshape(pr.shape[0], 1)
    
    Q_scaler, pr_scaler= MinMaxScaler(), MinMaxScaler()  # scaling wrt to max min
    
    Q_scaled = Q_scaler.fit_transform(Q)  # scaling
    pr_scaled = pr_scaler.fit_transform(pr)  # scaling
  
 
    Q_super1 = series_to_supervised(Q_scaled, n_lags) #need to delete first two columns and first two rows
    Q_super_values = Q_super1.values  # to make just values
    print(Q_super_values)
    
    pr_super = series_to_supervised(pr_scaled, n_lags)
    pr_super_values = pr_super.values  # to make just values
    print(pr_super_values)
    
    result = pd.concat([pr_super, Q_super1], axis=1)
    
    result=result.reset_index(inplace=False)
    result1=result.drop(['index'], axis = 1)#delete first two columns
    result1.columns
    
    X=result1.iloc[:,0:n_lags+1]
    Y=result1.iloc[:,n_lags+1:n_lags+2]
    
    train_X =X.iloc[:n_train, :]
    train_Y = Y.iloc[:n_train, :]
    
    test_X =X.iloc[n_train:, :]
    test_Y = Y.iloc[n_train:, :]
    
    print(np.shape(train_X), np.shape(train_Y),np.shape(test_X),np.shape(test_Y))
    
    train_X=train_X.values
    train_Y=train_Y.values
    test_X=test_X.values
    test_Y=test_Y.values
    
    # reshape 
    # 2D into 3D 
    train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
    train_Y = train_Y.reshape((train_Y.shape[0], 1, train_Y.shape[1]))
    test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
    test_Y = test_Y.reshape((test_Y.shape[0], 1, test_Y.shape[1]))
    print(np.shape(train_X), np.shape(train_Y), np.shape(test_X), np.shape(test_Y))
    return train_X, train_Y, test_X, test_Y, test_dates

def create_model(train_X, train_Y, test_X, test_Y):
    def rmse(y_true, y_pred):
        return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))
    
    n_test = 60
    n_epochs = 1000
    n_batch = 360

    os.environ['PYTHONHASHSEED'] = '0'
    np.random.seed(42)
    rn.seed(12345)
    session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
    tf.set_random_seed(1234)
    sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)
    K.set_session(sess)

    model = Sequential()
    model.add(LSTM(units=, activation=,
                   input_shape=(None, train_X.shape[2]), use_bias=True,
                   bias_regularizer=L1L2(l1=0.01, l2=0.01)))
    model.add(Dropout())
    model.add(Dense(activation='linear', units=1, use_bias=True))

    adam = keras.optimizers.Adam(lr=)
    sgd = keras.optimizers.SGD(lr=)
    rmsprop = keras.optimizers.RMSprop(lr=)
    
    choiceval = 
    if choiceval == 'adam':
        optim = adam
    elif choiceval == 'sgd':
        optim = sgd
    else:
        optim = rmsprop

    model.compile(loss=rmse, optimizer=optim)

    earlystop = keras.callbacks.EarlyStopping(monitor='loss', min_delta=0.00000001, patience=5, verbose=1, mode='auto')
    model.fit(train_X, train_Y, batch_size=n_batch, epochs=n_epochs, verbose=2, shuffle=False, callbacks=[earlystop])
    loss = model.evaluate(test_X, test_Y, batch_size=n_test, verbose=0)
    return {'loss': loss, 'status': STATUS_OK, 'model': model}

if __name__ == '__main__':
    n_lags = 12
     
    path = "D:/Binata/LSTM_23.5.2022/Code/"

    best_run, best_model = optim.minimize(model=create_model, data=data, algo=tpe.suggest, max_evals=100, trials=Trials())
    train_X, train_Y, test_X, test_Y, test_dates = data()
    print("Best performing model chosen hyper-parameters:")
    print(best_run)

It works fine without Optimization. After adding optimization, I am having an error as below. I am new in Coding. Can you help me please? Its urgent.

InvalidArgumentError: Expected multiples argument to be a vector of length 1 but got length 2 [[Node: training_7/SGD/gradients/loss_7/dense_9_loss/Mean_3_grad/Tile = Tile[T=DT_FLOAT, Tmultiples=DT_INT32, _class=["loc:@training_7/SGD/gradients/loss_7/dense_9_loss/Mean_3_grad/truediv"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](training_7/SGD/gradients/loss_7/dense_9_loss/Mean_3_grad/Reshape, training_7/SGD/gradients/loss_7/dense_9_loss/Mean_3_grad/Shape)]] [[Node: loss_7/add/_75 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_1654_loss_7/add", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

source https://stackoverflow.com/questions/72664212/invalidargumenterror-expected-multiples-argument-to-be-a-vector-of-length-1-but