ModelTrainer API cannot configure the right Session

**Describe the bug**
I'm trying to train a model using the `sagemaker.modules.train.ModelTrainer` API. However, it keeps trying to validate the SageMaker session using Pydantic, only never to accept any possible input. It spits out the Validation Error you see in the screenshot attached below.

**To reproduce**
1. Write a ModelTrainer compatible script
2. Write the following code:

```python
model_trainer = ModelTrainer(
training_image=image_uri, compute=compute, source_code=source_code,
hyperparameters=hyperparameters, environment=env,
base_job_name=job_prefix,
stopping_condition=StoppingCondition(max_runtime_in_seconds=90000),
checkpoint_config=CheckpointConfig(s3_uri=f"{checkpoint_s3_path}/{job_prefix}"),
)
model_trainer.fit(...)
```

**Expected behavior**
Training to start

**Screenshots or logs**

**System information**
A description of your system. Please provide:
- **SageMaker Python SDK version**: 2.248.0
- **Framework name (eg. PyTorch) or algorithm (eg. KMeans)**: N/A
- **Framework version**: N/A
- **Python version**: 3.12
- **CPU or GPU**: GPU
- **Custom Docker image (Y/N)**: N

**Additional context**
Tried to downgrade to other SageMaker SDK versions, but couldn't get to a working one.

Commencez par l’API ModelTrainer et reproduisez l’exemple fourni de fit(...) avec SageMaker Python SDK 2.248.0 et Python 3.12. Suivez la validation Pydantic de la session SageMaker et déterminez quelle entrée de session valide est acceptée ; le travail est terminé lorsque ModelTrainer.fit(...) démarre l’entraînement sans erreur de validation.

Rédigé par le modèle d'indexation à partir du texte de l'issue.