how to get the right cardinality of a dataset for lstm tensorflow network?

i am trying to make some clasification on a data, i have my X and y and they look like this

this is the X info

in the y i have a pandas dataframe of 1 col, which can contain an 1, 0 or -1, also the column position in X can be 1,0,-1

so i try to preprocess the data like this

X_train, y_train = X.iloc[0:107452], y.iloc[0:107452]
X_test, y_test = X.iloc[107452:len(X)], y.iloc[107452:len(y)]

y_train = tf.keras.utils.to_categorical(y_train)

y_test = tf.keras.utils.to_categorical(y_test)

X_train= np.asarray(X_train).astype('float32')
X_train=X_train.reshape(-1, 107452, 9)
y_train= np.asarray(y_train).astype('float32')
y_train=y_train.reshape(-1, 107452, 1)
X_test = np.asarray(X_test).astype('float32')

X_test=X_test.reshape(-1, 46050, 9)

y_test= np.asarray(y_test).astype('float32')
y_test=y_test.reshape(-1, 46050, 1)

to make the y a categorical. so the shape of the X(train and test) and y are

X_train shape: (1, 107452, 9), y_train shape: (2, 107452, 1)
X_test shape: (1, 46050, 9), y_test shape: (2, 46050, 1)

for the model i used this

model = keras.Sequential()
model.add(keras.layers.LSTM(250, input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(keras.layers.Dropout(0.2))
model.add(keras.layers.Dense(3,activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
model.summary()

history = model.fit(
    X_train, y_train,
    epochs=32,
    batch_size=32,
    shuffle=False
)

but i get the next error

ValueError: Data cardinality is ambiguous:
  x sizes: 1
  y sizes: 2
Make sure all arrays contain the same number of samples.

hope any of you can help me to figure out how to make it work, in advance thanks