code: def train_model(
      self, num_epochs=300, num_workers=1, early_stop=False, tenacity=10
  ):
      if num_workers == 1:
          return self.train_model_local(
              num_epochs=num_epochs,
              early_stop=early_stop,
              tenacity=tenacity,
          )
      else:
          from zoo.chronos.model.tcmf.local_model_distributed_trainer import (
              train_yseq_hvd,
          )
          import ray
  
          # check whether there has been an activate ray context yet.
          from zoo.ray import RayContext
  
          ray_ctx = RayContext.get()
          Ymat_id = ray.put(self.Ymat)
          covariates_id = ray.put(self.covariates)
          Ycov_id = ray.put(self.Ycov)
          trainer_config_keys = [
              "vbsize",
              "hbsize",
              "end_index",
              "val_len",
              "lr",
              "num_inputs",
              "num_channels",
              "kernel_size",
              "dropout",
          ]
          trainer_config = {
              k: self.__dict__[k] for k in trainer_config_keys
          }
          model, val_loss = train_yseq_hvd(
              epochs=num_epochs,
              workers_per_node=num_workers // ray_ctx.num_ray_nodes,
              Ymat_id=Ymat_id,
              covariates_id=covariates_id,
              Ycov_id=Ycov_id,
              **trainer_config,
          )
          self.seq = model
          return val_loss
  
imports: import pickle
  import numpy
  import torch
  import torch.nn
  import torch.optim
  from torch.autograd import Variable
  from torch.nn.utils import weight_norm
  from zoo.chronos.model.tcmf.data_loader import TCMFDataLoader
  from zoo.chronos.model.tcmf.time import TimeCovariates
  import logging
label_desc: Use image node_id 2 for calling the LocalModel obj's underlying member method code with example usage: obj.train_model(num_epochs, num_workers, early_stop, tenacity) and returns: self, val_loss