    def from_partial_objects(
        cls,
        serialization_dir: str,
        local_rank: int,
        dataset_reader: DatasetReader,
        train_data_path: Any,
        model: Lazy[Model],
        data_loader: Lazy[DataLoader],
        trainer: Lazy[Trainer],
        vocabulary: Lazy[Vocabulary] = Lazy(Vocabulary),
        datasets_for_vocab_creation: List[str] = None,
        validation_dataset_reader: DatasetReader = None,
        validation_data_path: Any = None,
        validation_data_loader: Lazy[DataLoader] = None,
        test_data_path: Any = None,
        evaluate_on_test: bool = False,
        batch_weight_key: str = "",
        ddp_accelerator: Optional[DdpAccelerator] = None,
    ) -> "TrainModel":
        """
        This method is intended for use with our `FromParams` logic, to construct a `TrainModel`
        object from a config file passed to the `allennlp train` command.  The arguments to this
        method are the allowed top-level keys in a configuration file (except for the first three,
        which are obtained separately).

        You *could* use this outside of our `FromParams` logic if you really want to, but there
        might be easier ways to accomplish your goal than instantiating `Lazy` objects.  If you are
        writing your own training loop, we recommend that you look at the implementation of this
        method for inspiration and possibly some utility functions you can call, but you very likely
        should not use this method directly.

        The `Lazy` type annotations here are a mechanism for building dependencies to an object
        sequentially - the `TrainModel` object needs data, a model, and a trainer, but the model
        needs to see the data before it's constructed (to create a vocabulary) and the trainer needs
        the data and the model before it's constructed.  Objects that have sequential dependencies
        like this are labeled as `Lazy` in their type annotations, and we pass the missing
        dependencies when we call their `construct()` method, which you can see in the code below.

        # Parameters

        serialization_dir: `str`
            The directory where logs and model archives will be saved.

            In a typical AllenNLP configuration file, this parameter does not get an entry as a
            top-level key, it gets passed in separately.

        local_rank: `int`
            The process index that is initialized using the GPU device id.

            In a typical AllenNLP configuration file, this parameter does not get an entry as a
            top-level key, it gets passed in separately.

        dataset_reader: `DatasetReader`
            The `DatasetReader` that will be used for training and (by default) for validation.

        train_data_path: `str`
            The file (or directory) that will be passed to `dataset_reader.read()` to construct the
            training data.

        model: `Lazy[Model]`
            The model that we will train.  This is lazy because it depends on the `Vocabulary`;
            after constructing the vocabulary we call `model.construct(vocab=vocabulary)`.

        data_loader: `Lazy[DataLoader]`
            The data_loader we use to batch instances from the dataset reader at training and (by
            default) validation time. This is lazy because it takes a dataset in it's constructor.

        trainer: `Lazy[Trainer]`
            The `Trainer` that actually implements the training loop.  This is a lazy object because
            it depends on the model that's going to be trained.

        vocabulary: `Lazy[Vocabulary]`, optional (default=`Lazy(Vocabulary)`)
            The `Vocabulary` that we will use to convert strings in the data to integer ids (and
            possibly set sizes of embedding matrices in the `Model`).  By default we construct the
            vocabulary from the instances that we read.

        datasets_for_vocab_creation: `List[str]`, optional (default=`None`)
            If you pass in more than one dataset but don't want to use all of them to construct a
            vocabulary, you can pass in this key to limit it.  Valid entries in the list are
            "train", "validation" and "test".

        validation_dataset_reader: `DatasetReader`, optional (default=`None`)
            If given, we will use this dataset reader for the validation data instead of
            `dataset_reader`.

        validation_data_path: `str`, optional (default=`None`)
            If given, we will use this data for computing validation metrics and early stopping.

        validation_data_loader: `Lazy[DataLoader]`, optional (default=`None`)
            If given, the data_loader we use to batch instances from the dataset reader at
            validation and test time. This is lazy because it takes a dataset in it's constructor.

        test_data_path: `str`, optional (default=`None`)
            If given, we will use this as test data.  This makes it available for vocab creation by
            default, but nothing else.

        evaluate_on_test: `bool`, optional (default=`False`)
            If given, we will evaluate the final model on this data at the end of training.  Note
            that we do not recommend using this for actual test data in every-day experimentation;
            you should only very rarely evaluate your model on actual test data.

        batch_weight_key: `str`, optional (default=`""`)
            The name of metric used to weight the loss on a per-batch basis.  This is only used
            during evaluation on final test data, if you've specified `evaluate_on_test=True`.

        ddp_accelerator : `Optional[DdpAccelerator]`, optional (default = `None`)
            A `DdpAccelerator` to use in distributed trainer. Passed to the model and the trainer.

        """
        # Train data loader.
        data_loaders: Dict[str, DataLoader] = {
            "train": data_loader.construct(reader=dataset_reader, data_path=train_data_path)
        }

        # Validation data loader.
        if validation_data_path is not None:
            validation_dataset_reader = validation_dataset_reader or dataset_reader
            if validation_data_loader is not None:
                data_loaders["validation"] = validation_data_loader.construct(
                    reader=validation_dataset_reader, data_path=validation_data_path
                )
            else:
                data_loaders["validation"] = data_loader.construct(
                    reader=validation_dataset_reader, data_path=validation_data_path
                )
                if getattr(data_loaders["validation"], "batches_per_epoch", None) is not None:
                    warnings.warn(
                        "Using 'data_loader' params to construct validation data loader since "
                        "'validation_data_loader' params not specified, but you have "
                        "'data_loader.batches_per_epoch' set which may result in different "
                        "validation datasets for each epoch.",
                        UserWarning,
                    )

        # Test data loader.
        if test_data_path is not None:
            test_dataset_reader = validation_dataset_reader or dataset_reader
            if validation_data_loader is not None:
                data_loaders["test"] = validation_data_loader.construct(
                    reader=test_dataset_reader, data_path=test_data_path
                )
            else:
                data_loaders["test"] = data_loader.construct(
                    reader=test_dataset_reader, data_path=test_data_path
                )

        if datasets_for_vocab_creation:
            for key in datasets_for_vocab_creation:
                if key not in data_loaders:
                    raise ConfigurationError(f"invalid 'dataset_for_vocab_creation' {key}")

            logger.info(
                "From dataset instances, %s will be considered for vocabulary creation.",
                ", ".join(datasets_for_vocab_creation),
            )

        instance_generator = (
            instance
            for key, data_loader in data_loaders.items()
            if datasets_for_vocab_creation is None or key in datasets_for_vocab_creation
            for instance in data_loader.iter_instances()
        )

        vocabulary_ = vocabulary.construct(instances=instance_generator)

        model_ = model.construct(
            vocab=vocabulary_, serialization_dir=serialization_dir, ddp_accelerator=ddp_accelerator
        )

        # Initializing the model can have side effect of expanding the vocabulary.
        # Save the vocab only in the primary. In the degenerate non-distributed
        # case, we're trivially the primary. In the distributed case this is safe
        # to do without worrying about race conditions since saving and loading
        # the vocab involves acquiring a file lock.
        if local_rank == 0:
            vocabulary_path = os.path.join(serialization_dir, "vocabulary")
            vocabulary_.save_to_files(vocabulary_path)

        for data_loader_ in data_loaders.values():
            data_loader_.index_with(model_.vocab)

        trainer_ = trainer.construct(
            serialization_dir=serialization_dir,
            model=model_,
            data_loader=data_loaders["train"],
            validation_data_loader=data_loaders.get("validation"),
            local_rank=local_rank,
            ddp_accelerator=ddp_accelerator,
        )
        assert trainer_ is not None

        return cls(
            serialization_dir=serialization_dir,
            model=model_,
            trainer=trainer_,
            evaluation_data_loader=data_loaders.get("test"),
            evaluate_on_test=evaluate_on_test,
            batch_weight_key=batch_weight_key,
        )
This method is intended for use with our `FromParams` logic, to construct a `TrainModel`
object from a config file passed to the `allennlp train` command.  The arguments to this
method are the allowed top-level keys in a configuration file (except for the first three,
which are obtained separately).

You *could* use this outside of our `FromParams` logic if you really want to, but there
might be easier ways to accomplish your goal than instantiating `Lazy` objects.  If you are
writing your own training loop, we recommend that you look at the implementation of this
method for inspiration and possibly some utility functions you can call, but you very likely
should not use this method directly.

The `Lazy` type annotations here are a mechanism for building dependencies to an object
sequentially - the `TrainModel` object needs data, a model, and a trainer, but the model
needs to see the data before it's constructed (to create a vocabulary) and the trainer needs
the data and the model before it's constructed.  Objects that have sequential dependencies
like this are labeled as `Lazy` in their type annotations, and we pass the missing
dependencies when we call their `construct()` method, which you can see in the code below.

# Parameters

serialization_dir: `str`
The directory where logs and model archives will be saved.

In a typical AllenNLP configuration file, this parameter does not get an entry as a
top-level key, it gets passed in separately.

local_rank: `int`
The process index that is initialized using the GPU device id.

In a typical AllenNLP configuration file, this parameter does not get an entry as a
top-level key, it gets passed in separately.

dataset_reader: `DatasetReader`
The `DatasetReader` that will be used for training and (by default) for validation.

train_data_path: `str`
The file (or directory) that will be passed to `dataset_reader.read()` to construct the
training data.

model: `Lazy[Model]`
The model that we will train.  This is lazy because it depends on the `Vocabulary`;
after constructing the vocabulary we call `model.construct(vocab=vocabulary)`.

data_loader: `Lazy[DataLoader]`
The data_loader we use to batch instances from the dataset reader at training and (by
default) validation time. This is lazy because it takes a dataset in it's constructor.

trainer: `Lazy[Trainer]`
The `Trainer` that actually implements the training loop.  This is a lazy object because
it depends on the model that's going to be trained.

vocabulary: `Lazy[Vocabulary]`, optional (default=`Lazy(Vocabulary)`)
The `Vocabulary` that we will use to convert strings in the data to integer ids (and
possibly set sizes of embedding matrices in the `Model`).  By default we construct the
vocabulary from the instances that we read.

datasets_for_vocab_creation: `List[str]`, optional (default=`None`)
If you pass in more than one dataset but don't want to use all of them to construct a
vocabulary, you can pass in this key to limit it.  Valid entries in the list are
"train", "validation" and "test".

validation_dataset_reader: `DatasetReader`, optional (default=`None`)
If given, we will use this dataset reader for the validation data instead of
`dataset_reader`.

validation_data_path: `str`, optional (default=`None`)
If given, we will use this data for computing validation metrics and early stopping.

validation_data_loader: `Lazy[DataLoader]`, optional (default=`None`)
If given, the data_loader we use to batch instances from the dataset reader at
validation and test time. This is lazy because it takes a dataset in it's constructor.

test_data_path: `str`, optional (default=`None`)
If given, we will use this as test data.  This makes it available for vocab creation by
default, but nothing else.

evaluate_on_test: `bool`, optional (default=`False`)
If given, we will evaluate the final model on this data at the end of training.  Note
that we do not recommend using this for actual test data in every-day experimentation;
you should only very rarely evaluate your model on actual test data.

batch_weight_key: `str`, optional (default=`""`)
The name of metric used to weight the loss on a per-batch basis.  This is only used
during evaluation on final test data, if you've specified `evaluate_on_test=True`.

ddp_accelerator : `Optional[DdpAccelerator]`, optional (default = `None`)
A `DdpAccelerator` to use in distributed trainer. Passed to the model and the trainer.
allennlp/commands/train.py