code: def train_model(
      model_names: List[str],
      dataset: BaseDataset,
      ensemble_type: str,
      csv: str,
      config_fname: str,
      retrain_type: str = "without_retrain",
      n_retrain: int = 10,
      load_checkpoint: bool = False,
      visualize: bool = False,
  ):
      """
      Trains all the model on the dataset, and evaluates its predictions for every
      horizon setting on every time series.
      """
      model_names = [resolve_model_name(m) for m in model_names]
      dirname = get_dirname(model_names, ensemble_type)
      dirname = dirname + "_" + retrain_type + str(n_retrain)
      results_dir = os.path.join(
          MERLION_ROOT, "results", "forecast", dirname
      )
      os.makedirs(results_dir, exist_ok=True)
      dataset_name = get_dataset_name(dataset)
  
      # Determine where to start within the dataset if there is a checkpoint
      if os.path.isfile(csv) and load_checkpoint:
          i0 = pd.read_csv(csv).idx.max()
      else:
          i0 = -1
          os.makedirs(os.path.dirname(csv), exist_ok=True)
          with open(csv, "w") as f:
              f.write(
                  "idx,name,horizon,retrain_type,n_retrain,RMSE,sMAPE\n"
              )
  
      model = None
      # loop over dataset
  
      is_multivariate_data = dataset[0][0].shape[1] > 1
  
      for i, (df, md) in enumerate(
          tqdm.tqdm(dataset, desc=f"{dataset_name} Dataset")
      ):
          if i <= i0:
              continue
          trainval = md["trainval"]
  
          # Resample to an appropriate granularity according to metadata
          if "granularity" in md:
              dt = md["granularity"]
              df = (
                  df.resample(dt, closed="right", label="right")
                  .mean()
                  .interpolate()
              )
  
          vals = TimeSeries.from_pd(df)
          dt = infer_granularity(vals.time_stamps)
  
          # Get the train/val split
          t = trainval.index[np.argmax(~trainval)].value // 1e9
          train_vals, test_vals = vals.bisect(t, t_in_left=False)
  
          # Compute train_window_len and test_window_len
          train_start_timestamp = train_vals.univariates[
              train_vals.names[0]
          ].time_stamps[0]
          test_start_timestamp = test_vals.univariates[
              test_vals.names[0]
          ].time_stamps[0]
          train_window_len = (
              test_start_timestamp - train_start_timestamp
          )
  
          train_end_timestamp = train_vals.univariates[
              train_vals.names[0]
          ].time_stamps[-1]
          test_end_timestamp = test_vals.univariates[
              test_vals.names[0]
          ].time_stamps[-1]
          test_window_len = test_end_timestamp - train_end_timestamp
  
          # Get all the horizon conditions we want to evaluate from metadata
          if any(
              "condition" in k and isinstance(v, list)
              for k, v in md.items()
          ):
              conditions = sum(
                  [
                      v
                      for k, v in md.items()
                      if "condition" in k and isinstance(v, list)
                  ],
                  [],
              )
              logger.debug(
                  "\n"
                  + "=" * 80
                  + "\n"
                  + df.columns[0]
                  + "\n"
                  + "=" * 80
                  + "\n"
              )
              horizons = set()
              for condition in conditions:
                  horizons.update(
                      [
                          v
                          for k, v in condition.items()
                          if "horizon" in k
                      ]
                  )
  
          # For multivariate data, we use a horizon of 3
          elif is_multivariate_data:
              horizons = [3 * dt]
  
          # For univariate data, we predict the entire test data in batch
          else:
              horizons = [test_window_len]
  
          # loop over horizon conditions
          for horizon in horizons:
              horizon = granularity_str_to_seconds(horizon)
              try:
                  max_forecast_steps = int(
                      math.ceil(horizon / dt.total_seconds())
                  )
              except:
                  window = TimeSeries.from_pd(
                      test_vals.to_pd()[
                          : to_pd_datetime(
                              train_end_timestamp + horizon
                          )
                      ]
                  )
                  max_forecast_steps = len(
                      TemporalResample(granularity=dt)(window)
                  )
              logger.debug(
                  f"horizon is {pd.Timedelta(seconds=horizon)} and max_forecast_steps is {max_forecast_steps}"
              )
              if retrain_type == "without_retrain":
                  retrain_freq = None
                  train_window = None
                  n_retrain = 0
              elif retrain_type == "sliding_window_retrain":
                  retrain_freq = math.ceil(
                      test_window_len / int(n_retrain)
                  )
                  train_window = train_window_len
                  horizon = min(retrain_freq, horizon)
              elif retrain_type == "expanding_window_retrain":
                  retrain_freq = math.ceil(
                      test_window_len / int(n_retrain)
                  )
                  train_window = None
                  horizon = min(retrain_freq, horizon)
              else:
                  raise ValueError(
                      "the retrain_type should be without_retrain, sliding_window_retrain or expanding_window_retrain"
                  )
  
              # Get Model
              models = [
                  get_model(
                      m, dataset, max_forecast_steps=max_forecast_steps
                  )
                  for m in model_names
              ]
              if len(models) == 1:
                  model = models[0]
              else:
                  config = ForecasterEnsembleConfig(
                      combiner=get_combiner(ensemble_type)
                  )
                  model = ForecasterEnsemble(
                      config=config, models=models
                  )
  
              evaluator = ForecastEvaluator(
                  model=model,
                  config=ForecastEvaluatorConfig(
                      train_window=train_window,
                      horizon=horizon,
                      retrain_freq=retrain_freq,
                  ),
              )
  
              # Get Evaluate Results
              train_result, test_pred = evaluator.get_predict(
                  train_vals=train_vals, test_vals=test_vals
              )
  
              rmses = evaluator.evaluate(
                  ground_truth=test_vals,
                  predict=test_pred,
                  metric=ForecastMetric.RMSE,
              )
              smapes = evaluator.evaluate(
                  ground_truth=test_vals,
                  predict=test_pred,
                  metric=ForecastMetric.sMAPE,
              )
  
              # Log relevant info to the CSV
              with open(csv, "a") as f:
                  f.write(
                      f"{i},{df.columns[0]},{horizon},{retrain_type},{n_retrain},{rmses},{smapes}\n"
                  )
  
              # generate comparison plot
              if visualize:
                  name = train_vals.names[0]
                  train_time_stamps = train_vals.univariates[
                      name
                  ].time_stamps
                  fig_dir = os.path.join(
                      results_dir, dataset_name + "_figs"
                  )
                  os.makedirs(fig_dir, exist_ok=True)
                  fig_dataset_dir = os.path.join(fig_dir, df.columns[0])
                  os.makedirs(fig_dataset_dir, exist_ok=True)
                  if train_result[0] is not None:
                      train_pred = train_result[0]
                  else:
                      train_pred = TimeSeries(
                          {
                              name: UnivariateTimeSeries(
                                  train_time_stamps, None
                              )
                          }
                      )
                  fig_name = (
                      dirname
                      + "_"
                      + retrain_type
                      + str(n_retrain)
                      + "_"
                      + "horizon"
                      + str(int(horizon))
                      + ".png"
                  )
                  plot_unrolled_compare(
                      train_vals,
                      test_vals,
                      train_pred,
                      test_pred,
                      os.path.join(fig_dataset_dir, fig_name),
                      dirname + f"(sMAPE={smapes:.4f})",
                  )
  
              # Log relevant info to the logger
              logger.debug(
                  f"{dirname} {retrain_type} {n_retrain} sMAPE : {smapes:.4f}\n"
              )
  
      # Save full experimental config
      if model is not None:
          full_config = dict(
              model_config=model.config.to_dict(),
              evaluator_config=evaluator.config.to_dict(),
              code_version_info=get_code_version_info(),
          )
  
          with open(config_fname, "w") as f:
              json.dump(full_config, f, indent=2, sort_keys=True)
  
imports: import argparse
  from collections import OrderedDict
  import glob
  import json
  import logging
  import math
  import os
  import re
  import sys
  import git
  from typing import Dict, List
  import numpy
  import pandas
  import tqdm
  from merlion.evaluate.forecast import ForecastEvaluator, ForecastMetric, ForecastEvaluatorConfig
  from merlion.models.ensemble.combine import CombinerBase, Mean, ModelSelector, MetricWeightedMean
  from merlion.models.ensemble.forecast import ForecasterEnsembleConfig, ForecasterEnsemble
  from merlion.models.factory import ModelFactory
  from merlion.models.forecast.base import ForecasterBase
  from merlion.transform.resample import TemporalResample, granularity_str_to_seconds
  from merlion.utils import TimeSeries, UnivariateTimeSeries
  from merlion.utils.resample import infer_granularity, to_pd_datetime
  from ts_datasets.base import BaseDataset
  from ts_datasets.forecast import *
  import matplotlib.pyplot
label_desc: Use image node_id 7 for calling a global function with example usage: train_model(model_names, dataset, ensemble_type, csv, config_fname, retrain_type, n_retrain, load_checkpoint, visualize) without return types