How to train a LSTM with a sequence of numbers with different lenghts?

Question

I am trying to train a LSTM with a dataset in which both the input and the output are a sequence of numbers of different lenght. Each number in the input represents a timestep. Example of input and output: Input:

         ent
229 [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 2.0, 2.0, ...
511 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 3.0, ...
110 [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 4.0, 4.0, ...
243 [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, ...
334 [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 2.0, 2.0, ...
Output:
         sal
229 [6.0, 7.0, 3.0, 0.0, 1.0, 4.0, 5.0, 2.0]
511 [0.0, 1.0, 6.0, 7.0, 2.0, 4.0, 5.0, 6.0, 7.0]
110 [3.0, 5.0, 0.0, 1.0, 5.0, 6.0, 7.0, 3.0]
243 [3.0, 6.0, 7.0, 4.0, 6.0, 7.0, 0.0, 1.0, 4.0]
334 [6.0, 7.0, 3.0, 4.0, 3.0, 5.0, 4.0]

When executing the train of the model always appears this error: ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray).

   model = keras.Sequential()

   model.add(layers.Input(shape=(None, 200))) 
   model.add(layers.LSTM(20))

Should I select a different NN or include padding? I have also tried changing the dimension to:

     ent
229 [[3.0], [3.0], [3.0], [3.0], [3.0], [3.0], [3....

Do you know what could I do?

   Traceback (most recent call last):
     at block 8, line 8
     at /opt/python/envs/default/lib/python3.8/site-packages/keras/utils/traceback_utils.pyline 67, in     error_handler(*args, **kwargs)
     at /opt/python/envs/default/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.pyline     106, in convert_to_eager_tensor(value, ctx, dtype)
   ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray).