Does the sequence length of a RNN/LSTM have to be the same for the input and output?

I have a question about the input and output data in a RNN or LSTM. A RNN expects a 3-dimensional vector as input of the form (Batch_size, sequence_length_input, features_input) and a 3-dimensional output vector of the form (Batch_size, sequence_length_output, features_output).

I know that the features_input and features_output don't have to have the same number. But what about the middle part sequence_length_input and sequence_length_output. Do they have to be the same? At least in my example (with Keras and Tensorflow) I always get an error if they are not the same. So I am wondering whetever I have a bug in the code or if this is generally not possible.

Update: Here is my code with a RNN for time series forecasting that only works if the sequence_length of the input steps_backward is equal to the sequence_length of the output steps_forward otherwise it will throw a ValueError:

ValueError: Dimensions must be equal, but are 192 and 96 for '{{node mean_squared_error/SquaredDifference}} = SquaredDifference[T=DT_FLOAT](sequential_5/time_distributed_5/Reshape_1, IteratorGetNext:1)' with input shapes: [?,192,1], [?,96,1].

In the code I use the 96 past timesteps (or 2*96=192 timesteps) to predict the future 96 timesteps. When the number of past and future timesteps are equal (equal sequence_length), everything works fine. Otherwise (unequal sequence_length) I get the ValueError.

#Import modules
import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import StandardScaler
from tensorflow import keras

# Define the parameters of the RNN and the training
epochs = 1
batch_size = 50
steps_backwards = 2 * 96
steps_forward = 96
split_fraction_trainingData = 0.70
split_fraction_validatinData = 0.90
randomSeedNumber = 50

#Read dataset
df = pd.read_csv('C:/Users/User1/Desktop/TestData.csv', sep=';', header=0, low_memory=False, infer_datetime_format=True, parse_dates={'datetime':[0]}, index_col=['datetime'])

# standardize data

data = df.values
indexWithYLabelsInData = 0
data_X = data[:, 0:3]
data_Y = data[:, indexWithYLabelsInData].reshape(-1, 1)


scaler_standardized_X = StandardScaler()
data_X = scaler_standardized_X.fit_transform(data_X)
data_X = pd.DataFrame(data_X)
scaler_standardized_Y = StandardScaler()
data_Y = scaler_standardized_Y.fit_transform(data_Y)
data_Y = pd.DataFrame(data_Y)


# Prepare the input data for the RNN

series_reshaped_X =  np.array([data_X[i:i + (steps_backwards+steps_forward)].copy() for i in range(len(data) - (steps_backwards+steps_forward))])
series_reshaped_Y =  np.array([data_Y[i:i + (steps_backwards+steps_forward)].copy() for i in range(len(data) - (steps_backwards+steps_forward))])


timeslot_x_train_end = int(len(series_reshaped_X)* split_fraction_trainingData)
timeslot_x_valid_end = int(len(series_reshaped_X)* split_fraction_validatinData)

X_train = series_reshaped_X[:timeslot_x_train_end, :steps_backwards] 
X_valid = series_reshaped_X[timeslot_x_train_end:timeslot_x_valid_end, :steps_backwards] 
X_test = series_reshaped_X[timeslot_x_valid_end:, :steps_backwards] 

   
Y_train = series_reshaped_Y[:timeslot_x_train_end, steps_backwards:] 
Y_valid = series_reshaped_Y[timeslot_x_train_end:timeslot_x_valid_end, steps_backwards:] 
Y_test = series_reshaped_Y[timeslot_x_valid_end:, steps_backwards:]                                
   
   
# Build the model and train it

np.random.seed(randomSeedNumber)
tf.random.set_seed(randomSeedNumber)

model = keras.models.Sequential([
    keras.layers.SimpleRNN(10, return_sequences=True, input_shape=[None, 3]),
    keras.layers.SimpleRNN(10, return_sequences=True),
    keras.layers.TimeDistributed(keras.layers.Dense(1))
])

model.compile(loss=mean_squared_error, optimizer=adam)
history = model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_valid, Y_valid))

#Predict the test data
Y_pred = model.predict(X_test)

and here is some test data https://filetransfer.io/data-package/ufbzh09o#link

Reminder: The code and the data provide a Minimal reproducible example. Maybe you can have a look at it as in this code the sequence_length has to be equal for the input and output data, otherwise I get an error. Unfortuantely I still have not figured out why this this problem occurs