code: def run_train_translate(
      train_params: str,
      translate_params: str,
      data: Dict[str, Any],
      use_prepared_data: bool = False,
      max_seq_len: int = 10,
      seed: int = 13,
  ) -> Dict[str, Any]:
      """
      Train a model and translate a test set. Returns the updated data dictionary containing paths to translation outputs
      and scores.
  
      :param train_params: Command line args for model training.
      :param translate_params: First command line args for translation.
      :param data: Dictionary containing test data
      :param use_prepared_data: Whether to use the prepared data functionality.
      :param max_seq_len: The maximum sequence length.
      :param seed: The seed used for training.
      :return: Data dictionary, updated with translation outputs and scores
      """
      work_dir = os.path.join(data["work_dir"], "train_translate")
      data["model"] = os.path.join(work_dir, "model")
      # Optionally create prepared data directory
      if use_prepared_data:
          data["train_prepared"] = os.path.join(
              work_dir, "prepared_data"
          )
          prepare_params = "{} {}".format(
              sockeye.prepare_data.__file__,
              PREPARE_DATA_COMMON.format(
                  train_source=data["train_source"],
                  train_target=data["train_target"],
                  output=data["train_prepared"],
                  max_len=max_seq_len,
              ),
          )
          if "train_source_factors" in data:
              prepare_params += TRAIN_WITH_SOURCE_FACTORS_COMMON.format(
                  source_factors=" ".join(data["train_source_factors"])
              )
          if "train_target_factors" in data:
              prepare_params += TRAIN_WITH_TARGET_FACTORS_COMMON.format(
                  target_factors=" ".join(data["train_target_factors"])
              )
  
          if "--weight-tying-type src_trg" in train_params:
              prepare_params += " --shared-vocab"
  
          logger.info(
              "Preparing data with parameters %s.", prepare_params
          )
          with patch.object(sys, "argv", prepare_params.split()):
              sockeye.prepare_data.main()
          # Train model
          params = "{} {} {}".format(
              sockeye.train.__file__,
              TRAIN_PARAMS_PREPARED_DATA_COMMON.format(
                  prepared_data=data["train_prepared"],
                  dev_source=data["dev_source"],
                  dev_target=data["dev_target"],
                  model=data["model"],
                  max_len=max_seq_len,
              ),
              train_params,
          )
  
          if "dev_source_factors" in data:
              params += DEV_WITH_SOURCE_FACTORS_COMMON.format(
                  dev_source_factors=" ".join(
                      data["dev_source_factors"]
                  )
              )
          if "dev_target_factors" in data:
              params += DEV_WITH_TARGET_FACTORS_COMMON.format(
                  dev_target_factors=" ".join(
                      data["dev_target_factors"]
                  )
              )
  
          logger.info(
              "Starting training with parameters %s.", train_params
          )
          with patch.object(sys, "argv", params.split()):
              sockeye.train.main()
      else:
          # Train model
          params = "{} {} {}".format(
              sockeye.train.__file__,
              TRAIN_PARAMS_COMMON.format(
                  train_source=data["train_source"],
                  train_target=data["train_target"],
                  dev_source=data["dev_source"],
                  dev_target=data["dev_target"],
                  model=data["model"],
                  max_len=max_seq_len,
                  seed=seed,
              ),
              train_params,
          )
  
          if "train_source_factors" in data:
              params += TRAIN_WITH_SOURCE_FACTORS_COMMON.format(
                  source_factors=" ".join(data["train_source_factors"])
              )
          if "train_target_factors" in data:
              params += TRAIN_WITH_TARGET_FACTORS_COMMON.format(
                  target_factors=" ".join(data["train_target_factors"])
              )
          if "dev_source_factors" in data:
              params += DEV_WITH_SOURCE_FACTORS_COMMON.format(
                  dev_source_factors=" ".join(
                      data["dev_source_factors"]
                  )
              )
          if "dev_target_factors" in data:
              params += DEV_WITH_TARGET_FACTORS_COMMON.format(
                  dev_target_factors=" ".join(
                      data["dev_target_factors"]
                  )
              )
  
          logger.info(
              "Starting training with parameters %s.", train_params
          )
          with patch.object(sys, "argv", params.split()):
              sockeye.train.main()
  
      # create Top-K lexicon from simple ttable mapping digit to digit
      ttable_path = os.path.join(data["work_dir"], "ttable")
      generate_fast_align_lex(ttable_path)
      lexicon_path = os.path.join(data["work_dir"], "lexicon")
      params = "{} {}".format(
          sockeye.lexicon.__file__,
          LEXICON_CREATE_PARAMS_COMMON.format(
              input=ttable_path,
              model=data["model"],
              topk=20,
              lexicon=lexicon_path,
          ),
      )
      with patch.object(sys, "argv", params.split()):
          sockeye.lexicon.main()
      data["lexicon"] = lexicon_path
  
      # Translate corpus with the 1st params and scoring output handler to obtain scores
      data["test_output"] = os.path.join(work_dir, "test.out")
      data["test_with_target_prefix_output"] = os.path.join(
          work_dir, "test_with_target_prefix.out"
      )
  
      # First set of params (with target prefix in JSON format)
      params = "{} {} {}".format(
          sockeye.translate.__file__,
          TRANSLATE_PARAMS_COMMON.format(
              model=data["model"],
              input=data["test_source_with_target_prefix"],
              output=data["test_with_target_prefix_output"],
          ),
          translate_params,
      )
      params += TRANSLATE_WITH_JSON_FORMAT
      logger.info("Translating with params %s", params)
      with patch.object(sys, "argv", params.split()):
          sockeye.translate.main()
  
      # Collect test translate outputs and scores
      data[
          "test_with_target_prefix_outputs"
      ] = collect_translate_output_and_scores(
          data["test_with_target_prefix_output"]
      )
  
      # Second set of params (without target prefix)
      params = "{} {} {}".format(
          sockeye.translate.__file__,
          TRANSLATE_PARAMS_COMMON.format(
              model=data["model"],
              input=data["test_source"],
              output=data["test_output"],
          ),
          translate_params,
      )
  
      if "test_source_factors" in data:
          params += TRANSLATE_WITH_FACTORS_COMMON.format(
              input_factors=" ".join(data["test_source_factors"])
          )
  
      logger.info("Translating with params %s", params)
      with patch.object(sys, "argv", params.split()):
          sockeye.translate.main()
  
      # Collect test inputs
      with open(data["test_source"]) as inputs:
          data["test_inputs"] = [line.strip() for line in inputs]
  
      # Collect test references
      with open(data["test_target"], "r") as ref:
          data["test_targets"] = [line.strip() for line in ref]
  
      # Collect test translate outputs and scores
      data["test_outputs"] = collect_translate_output_and_scores(
          data["test_output"]
      )
      assert (
          len(data["test_inputs"])
          == len(data["test_targets"])
          == len(data["test_outputs"])
          == len(data["test_with_target_prefix_outputs"])
      )
      return data
  
imports: import json
  import logging
  import os
  import random
  import sys
  from contextlib import contextmanager
  from tempfile import TemporaryDirectory
  from typing import Any, Dict, List, Optional
  from unittest.mock import patch
  import sockeye.constants
  import sockeye.prepare_data
  import sockeye.train
  import sockeye.translate
  import sockeye.lexicon
  import sockeye.utils
label_desc: Use image node_id 7 for calling a global function with example usage: run_train_translate(train_params, translate_params, data, use_prepared_data, max_seq_len, seed) and returns: data