class_docstr: SockeyeModel shares components needed for both training and inference.
  The main components of a Sockeye model are
  1) Source embedding
  2) Target embedding
  3) Encoder
  4) Decoder
  5) Output Layer
  
  ModelConfig contains parameters and their values that are fixed at training time and must be re-used at inference
  time.
  
  :param config: Model configuration.
  :param inference_only: Use the model only for inference, enabling optimizations.
  :param clamp_to_dtype: Avoid -inf/inf by clamping outputs to min/max finite values for their dtype.
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docstr: One step decoding of the translation model.
  
  :param step_input: Input to a single decoder step. Shape: (batch_size, num_target_factors).
  :param states: List of previous or initial model states. Shape of state tensors and length of states list
                 determined by self.decoder.state_structure().
  :param vocab_slice_ids: Optional list of vocabulary ids to use
                          for reduced matrix multiplication at the output layer.
  
  :return: logits, KNN output if present otherwise None, list of new model states, other target factor logits.
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code: def decode_step(
      self,
      step_input: pt.Tensor,
      states: List[pt.Tensor],
      vocab_slice_ids: Optional[pt.Tensor] = None,
  ) -> Tuple[pt.Tensor, pt.Tensor, List[pt.Tensor], List[pt.Tensor]]:
      """
      One step decoding of the translation model.
  
      :param step_input: Input to a single decoder step. Shape: (batch_size, num_target_factors).
      :param states: List of previous or initial model states. Shape of state tensors and length of states list
                     determined by self.decoder.state_structure().
      :param vocab_slice_ids: Optional list of vocabulary ids to use
                              for reduced matrix multiplication at the output layer.
  
      :return: logits, KNN output if present otherwise None, list of new model states, other target factor logits.
      """
      decode_step_inputs = [step_input, states]
      if vocab_slice_ids is not None:
          decode_step_inputs.append(vocab_slice_ids)
      if self.traced_decode_step is None:
          logger.debug("Tracing decode step")
          decode_step_module = _DecodeStep(
              self.embedding_target,
              self.decoder,
              self.output_layer,
              self.factor_output_layers,
              self.knn,
          )
          self.traced_decode_step = pt.jit.trace(
              decode_step_module, decode_step_inputs
          )
      # the traced module returns a flat list of tensors
      decode_step_outputs = self.traced_decode_step(*decode_step_inputs)
      # +1 for the decoder output, which will be used to generate kNN output
      (
          step_output,
          decoder_out,
          *target_factor_outputs,
      ) = decode_step_outputs[: self.num_target_factors + 1]
  
      # do the query here because it cannot be traced (jit.ignore does not play well with tracing)
      knn_output = (
          self.knn(decoder_out) if self.knn is not None else None
      )
  
      new_states = decode_step_outputs[self.num_target_factors + 1 :]
      return step_output, knn_output, new_states, target_factor_outputs
  
imports: import copy
  import logging
  import os
  import time
  from dataclasses import dataclass
  from functools import lru_cache
  from typing import cast, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from sockeye import __version__
  from .None import constants
  from .None import data_io
  from .None import decoder
  from .None import encoder
  from .None import layers
  from .None import transformer
  from .None import utils
  from .None import vocab
  from .config import Config
  from .encoder import FactorConfig
  from .layers import LengthRatioConfig
  from .None import nvs
  from sockeye.knn import KNNConfig
label_desc: Use image node_id 8 for calling the SockeyeModel obj's underlying member method code with example usage: obj.decode_step(step_input, states, vocab_slice_ids) and returns: step_output,  knn_output,  new_states,  target_factor_outputs