Input 0 of layer "conv2d_5" is incompatible with the layer: expected min_ndim=4, found ndim=2. Full shape received: (None, 2)

I am trying to use CNN on multivariate time series instead the most common usage on images. The number of features is between 90 and 120, depending on which I need to consider and experiment with. This is my code

scaler = StandardScaler()
X_train_s = scaler.fit_transform(X_train)
X_test_s = scaler.transform(X_test)

X_train_s = X_train_s.reshape((X_train_s.shape[0], X_train_s.shape[1],1))
X_test_s = X_test_s.reshape((X_test_s.shape[0], X_test_s.shape[1],1))

batch_size = 1024
length = 120
n_features = X_train_s.shape[1]

generator = TimeseriesGenerator(X_train_s, pd.DataFrame.to_numpy(Y_train[['TARGET_KEEP_LONG', 
                                                                          'TARGET_KEEP_SHORT']]), 
                                                                 length=length, 
                                                                 batch_size=batch_size)

validation_generator = TimeseriesGenerator(X_test_s, pd.DataFrame.to_numpy(Y_test[['TARGET_KEEP_LONG', 'TARGET_KEEP_SHORT']]), length=length, batch_size=batch_size)


early_stop = EarlyStopping(monitor = 'val_accuracy', mode = 'max', verbose = 1, patience = 20)

CNN_model = Sequential()
   
model.add(
    Conv2D(
        filters=64,
        kernel_size=(1, 5),
        strides=1,
        activation="relu",
        padding="valid",
        input_shape=(length, n_features, 1),
        use_bias=True,
    )
)
model.add(MaxPooling2D(pool_size=(1, 2)))
model.add(
    Conv2D(
        filters=64,
        kernel_size=(1, 5),
        strides=1,
        activation="relu",
        padding="valid",
        use_bias=True,
    )
)
[... code continuation ...]

In other words, I take the features as one dimension and a certain number of rows as the other dimension. But I get this error

"ValueError: Input 0 of layer "conv2d_5" is incompatible with the layer: expected min_ndim=4, found ndim=2. Full shape received: (None, 2)"

that is referred to the first CNN layer.

from Input 0 of layer "conv2d_5" is incompatible with the layer: expected min_ndim=4, found ndim=2. Full shape received: (None, 2)