class_docstr: This class exists so that we can easily read a configuration file with the `allennlp train`
  command.  The basic logic is that we call `train_loop =
  TrainModel.from_params(params_from_config_file)`, then `train_loop.run()`.  This class performs
  very little logic, pushing most of it to the `Trainer` that has a `train()` method.  The
  point here is to construct all of the dependencies for the `Trainer` in a way that we can do
  it using `from_params()`, while having all of those dependencies transparently documented and
  not hidden in calls to `params.pop()`.  If you are writing your own training loop, you almost
  certainly should not use this class, but you might look at the code for this class to see what
  we do, to make writing your training loop easier.
  
  In particular, if you are tempted to call the `__init__` method of this class, you are probably
  doing something unnecessary.  Literally all we do after `__init__` is call `trainer.train()`.  You
  can do that yourself, if you've constructed a `Trainer` already.  What this class gives you is a
  way to construct the `Trainer` by means of a config file.  The actual constructor that we use
  with `from_params` in this class is `from_partial_objects`.  See that method for a description
  of all of the allowed top-level keys in a configuration file used with `allennlp train`.
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code: def __init__(
      self,
      serialization_dir: str,
      model: Model,
      trainer: Trainer,
      evaluation_data_loader: DataLoader = None,
      evaluate_on_test: bool = False,
      batch_weight_key: str = "",
  ) -> None:
      self.serialization_dir = serialization_dir
      self.model = model
      self.trainer = trainer
      self.evaluation_data_loader = evaluation_data_loader
      self.evaluate_on_test = evaluate_on_test
      self.batch_weight_key = batch_weight_key
  
imports: import argparse
  import logging
  import os
  from os import PathLike
  from typing import Any, Dict, List, Optional, Union
  import warnings
  import torch
  import torch.distributed
  import torch.multiprocessing
  from allennlp.commands.subcommand import Subcommand
  from allennlp.common import Params, Registrable, Lazy
  from allennlp.common.checks import check_for_gpu, ConfigurationError
  from allennlp.common.meta import Meta, META_NAME
  from allennlp.common import logging
  from allennlp.common import util
  from allennlp.common.plugins import import_plugins
  from allennlp.data import DatasetReader, Vocabulary
  from allennlp.data import DataLoader
  from allennlp.models.archival import archive_model, CONFIG_NAME, verify_include_in_archive
  from allennlp.models.model import Model
  from allennlp.nn.parallel import DdpAccelerator
  from allennlp.training.trainer import Trainer
  from allennlp.training import util
label_desc: Use image node_id 1 to create a new TrainModel object from inherited base classes: Registrable with example: obj = TrainModel(serialization_dir, model, trainer, evaluation_data_loader, evaluate_on_test, batch_weight_key)