As I was fitting various artificial neural network, the code for TLNN was shown as incorrect especially the line containing the reshape function. Is there anything wrong with this? Does changing the data set imply that the reshape function wont work and shows this error

def forecast_TLNN(model, time_lagged_points, last_sequence, future_steps):
    forecasted_values = []
    max_lag = max(time_lagged_points)
    for i in range(future_steps):
        input_sequence = [last_sequence[max_lag - p] for p in time_lagged_points]
        forecasted_value = model.predict(np.reshape(input_sequence, (1, len(input_sequence))))
        forecasted_values.append(forecasted_value[0][0])
        last_sequence = last_sequence[1:] + [forecasted_value[0][0]]
    return forecasted_values

the error was shown in the line forecasted_value = model.predict(np.reshape(input_sequence, (1, len(input_sequence))))

I cant seem to find any corrections regarding this code all over the internet.

    ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    Cell In[83], line 13
     10 # look_back, hidden_nodes, output_nodes, epochs, batch_size, future_steps
     11 parameters_LSTM = [[1,2,3,4,5,6,7,8,9,10,11,12,13], [3,4,5,6], [1], [300], [20], [future_steps]]
---> 13 RMSE_info = compare_ANN_methods(rainfall_data, test_rainfall_data, scaler, parameters_FNN, parameters_TLNN, parameters_SANN, parameters_LSTM, future_steps)

Cell In[79], line 6, in compare_ANN_methods(rainfall_data, test_rainfall_data, scaler, parameters_FNN, parameters_TLNN, parameters_SANN, parameters_LSTM, future_steps)
      3 information_FNN_df = get_accuracies_FNN(rainfall_data, test_rainfall_data, parameters_FNN, scaler)
      4 optimized_params_FNN = analyze_results(information_FNN_df, test_rainfall_data, 'FNN')
----> 6 information_TLNN_df = get_accuracies_TLNN(rainfall_data, test_rainfall_data, parameters_TLNN, scaler)
      7 optimized_params_TLNN = analyze_results(information_TLNN_df, test_rainfall_data, 'TLNN')
      9 information_SANN_df = get_accuracies_SANN(rainfall_data, test_rainfall_data, parameters_SANN, scaler)

Cell In[55], line 21, in get_accuracies_TLNN(rainfall_data, test_rainfall_data, parameters, scaler)
     18 batch_size = param[4]
     19 future_steps = param[5]
---> 21 model_TLNN, forecasted_values_TLNN = TLNN(rainfall_data, time_lagged_points, hidden_nodes, output_nodes, epochs, batch_size, future_steps, scaler)
     23 y_true = test_rainfall_data.iloc[:future_steps].Precipitation
     24 mse, mae, mape, rmse = calculate_performance(y_true, forecasted_values_TLNN)

Cell In[53], line 9, in TLNN(data, time_lagged_points, hidden_nodes, output_nodes, epochs, batch_size, future_steps, scaler)
      6 model_TLNN = train_model(model_TLNN, X_train, y_train, epochs, batch_size)
      8 max_lag = max(time_lagged_points)
----> 9 forecasted_values_TLNN = forecast_TLNN(model_TLNN, time_lagged_points, 
     10                                   list(data[-max_lag:]), future_steps=future_steps)
     11 forecasted_values_TLNN = list(scaler.inverse_transform([forecasted_values_TLNN])[0])
     13 return model_TLNN, forecasted_values_TLNN

Cell In[51], line 6, in forecast_TLNN(model, time_lagged_points, last_sequence, future_steps)
      4 for i in range(future_steps):
      5     input_sequence = [last_sequence[max_lag - p] for p in time_lagged_points]
----> 6     forecasted_value = model.predict((np.reshape(input_sequence, (1, len(input_sequence)))))
      7     forecasted_values.append(forecasted_value[0][0])
      8     last_sequence = last_sequence[1:] + [forecasted_value[0][0]]

File ~anaconda3Libsite-packagesnumpycorefromnumeric.py:285, in reshape(a, newshape, order)
    200 @array_function_dispatch(_reshape_dispatcher)
    201 def reshape(a, newshape, order='C'):
    202     """
    203     Gives a new shape to an array without changing its data.
    204 
   (...)
    283            [5, 6]])
    284     """
--> 285     return _wrapfunc(a, 'reshape', newshape, order=order)

File ~anaconda3Libsite-packagesnumpycorefromnumeric.py:56, in _wrapfunc(obj, method, *args, **kwds)
     54 bound = getattr(obj, method, None)
     55 if bound is None:
---> 56     return _wrapit(obj, method, *args, **kwds)
     58 try:
     59     return bound(*args, **kwds)

File ~anaconda3Libsite-packagesnumpycorefromnumeric.py:45, in _wrapit(obj, method, *args, **kwds)
     43 except AttributeError:
     44     wrap = None
---> 45 result = getattr(asarray(obj), method)(*args, **kwds)
     46 if wrap:
     47     if not isinstance(result, mu.ndarray):

ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (5,) + inhomogeneous part.