code: def decode_step(
      self,
      step_input: pt.Tensor,
      states: List,
      vocab_slice_ids: Optional[pt.Tensor] = None,
      *args,
  ):
      batch_beam_size, num_target_factors = step_input.size()
      print("step_input", step_input)
  
      internal_lengths, num_decode_step_calls = states
      num_decode_step_calls = num_decode_step_calls.item()
      if (
          num_decode_step_calls == 0
      ):  # first call to decode_step, we expect step input to be all <bos>
          assert (step_input == C.BOS_ID).all()
  
      if step_input[:, 0].item() == C.BOS_ID:
          # predict word id 4 given <bos>
          scores = pt.tensor([0, 0, 0, 0, 1])
      elif step_input[:, 0].item() == C.EOS_ID:
          # predict pad given <eos>
          scores = pt.tensor([1, 0, 0, 0, 0])
      else:
          # otherwise always predict pad
          scores = pt.tensor([0, 0, 0, 0, 1])
  
      # topk is minimizing
      scores *= -1
  
      internal_lengths += 1
      num_decode_step_calls += 1
  
      self.states = states = [
          internal_lengths,
          pt.tensor([num_decode_step_calls], dtype=pt.int),
      ]
      return scores, states, None
  
imports: from typing import List, Optional
  from typing import Tuple
  import numpy
  import pytest
  import torch
  import numpy
  import sockeye.beam_search
  import sockeye.constants
  import sockeye.lexicon
  import sockeye.utils
label_desc: Use image node_id 4 for calling the _TestInference obj's underlying member method code with example usage: obj.decode_step(step_input, states, vocab_slice_ids) and returns: scores,  states,  None