    def ensure_model_can_train(
        self,
        trainer: GradientDescentTrainer,
        gradients_to_ignore: Set[str] = None,
        metric_to_check: str = None,
        metric_terminal_value: float = None,
        metric_tolerance: float = 1e-4,
        disable_dropout: bool = True,
    ):
        """
        A simple test for model training behavior when you are not using configuration files. In
        this case, we don't have a story around saving and loading models (you need to handle that
        yourself), so we don't have tests for that.  We just test that the model can train, and that
        it computes gradients for all parameters.

        Because the `Trainer` already has a reference to a model and to a data loader, we just take
        the `Trainer` object itself, and grab the `Model` and other necessary objects from there.

        # Parameters

        trainer: `GradientDescentTrainer`
            The `Trainer` to use for the test, which already has references to a `Model` and a
            `DataLoader`, which we will use in the test.
        gradients_to_ignore : `Set[str]`, optional (default=`None`)
            This test runs a gradient check to make sure that we're actually computing gradients
            for all of the parameters in the model.  If you really want to ignore certain
            parameters when doing that check, you can pass their names here.  This is not
            recommended unless you're `really` sure you don't need to have non-zero gradients for
            those parameters (e.g., some of the beam search / state machine models have
            infrequently-used parameters that are hard to force the model to use in a small test).
        metric_to_check: `str`, optional (default = `None`)
            We may want to automatically perform a check that model reaches given metric when
            training (on validation set, if it is specified). It may be useful in CI, for example.
            You can pass any metric that is in your model returned metrics.
        metric_terminal_value: `str`, optional (default = `None`)
            When you set `metric_to_check`, you need to set the value this metric must converge to
        metric_tolerance: `float`, optional (default=`1e-4`)
            Tolerance to check you model metric against metric terminal value. One can expect some
            variance in model metrics when the training process is highly stochastic.
        disable_dropout : `bool`, optional (default = `True`)
            If True we will set all dropout to 0 before checking gradients. (Otherwise, with small
            datasets, you may get zero gradients because of unlucky dropout.)
        """
        metrics = trainer.train()
        if metric_to_check is not None:
            metric_value = metrics.get(f"best_validation_{metric_to_check}") or metrics.get(
                f"training_{metric_to_check}"
            )
            assert metric_value is not None, f"Cannot find {metric_to_check} in metrics.json file"
            assert metric_terminal_value is not None, "Please specify metric terminal value"
            assert abs(metric_value - metric_terminal_value) < metric_tolerance

        model_batch = next(iter(trainer.data_loader))

        # Check gradients are None for non-trainable parameters and check that
        # trainable parameters receive some gradient if they are trainable.
        self.check_model_computes_gradients_correctly(
            trainer.model, model_batch, gradients_to_ignore, disable_dropout
        )
A simple test for model training behavior when you are not using configuration files. In
this case, we don't have a story around saving and loading models (you need to handle that
yourself), so we don't have tests for that.  We just test that the model can train, and that
it computes gradients for all parameters.

Because the `Trainer` already has a reference to a model and to a data loader, we just take
the `Trainer` object itself, and grab the `Model` and other necessary objects from there.

# Parameters

trainer: `GradientDescentTrainer`
The `Trainer` to use for the test, which already has references to a `Model` and a
`DataLoader`, which we will use in the test.
gradients_to_ignore : `Set[str]`, optional (default=`None`)
This test runs a gradient check to make sure that we're actually computing gradients
for all of the parameters in the model.  If you really want to ignore certain
parameters when doing that check, you can pass their names here.  This is not
recommended unless you're `really` sure you don't need to have non-zero gradients for
those parameters (e.g., some of the beam search / state machine models have
infrequently-used parameters that are hard to force the model to use in a small test).
metric_to_check: `str`, optional (default = `None`)
We may want to automatically perform a check that model reaches given metric when
training (on validation set, if it is specified). It may be useful in CI, for example.
You can pass any metric that is in your model returned metrics.
metric_terminal_value: `str`, optional (default = `None`)
When you set `metric_to_check`, you need to set the value this metric must converge to
metric_tolerance: `float`, optional (default=`1e-4`)
Tolerance to check you model metric against metric terminal value. One can expect some
variance in model metrics when the training process is highly stochastic.
disable_dropout : `bool`, optional (default = `True`)
If True we will set all dropout to 0 before checking gradients. (Otherwise, with small
datasets, you may get zero gradients because of unlucky dropout.)
allennlp/common/testing/model_test_case.py