ModelTrainer doesn't support heterogeneous clusters

**Describe the bug**
Seems there is a bug with [ModelTrainer](https://sagemaker.readthedocs.io/en/stable/api/training/model_trainer.html) and [heterogeneous clusters](https://docs.aws.amazon.com/sagemaker/latest/dg/train-heterogeneous-cluster-configure.html). When I'm trying to run a SageMaker training job with an heterogeneous clusters, even if I'm configuring the instance_group in the compute, and in the input channels, I get the following error:

```
ClientError: An error occurred (ValidationException) when calling the CreateTrainingJob operation: Some channels
have assigned instance groups: [test, train] while others not: [sm_drivers, code]
```

`sm_drivers` and `code` are private channels configured by the SDK.

**To reproduce**
A clear, step-by-step set of instructions to reproduce the bug.

```
from sagemaker.modules.configs import InstanceGroup
from sagemaker.modules.configs import InputData, S3DataSource
from sagemaker.modules.configs import (
Compute,
OutputDataConfig,
RemoteDebugConfig,
SourceCode,
StoppingCondition,
)
from sagemaker.modules.train import ModelTrainer

group_1 = InstanceGroup(
instance_type="ml.g5.xlarge",
instance_count=2,
instance_group_name="group_1",
)

group_2 = InstanceGroup(
instance_type="ml.t3.xlarge",
instance_count=1,
instance_group_name="group_2",
)

# Define the script to be run
source_code = SourceCode(
source_dir="./scripts",
requirements="requirements.txt",
command="python launcher.py -e train.py",
)

# Define the compute
compute_configs = Compute(
instance_groups=[group_1, group_2]
keep_alive_period_in_seconds=0,
)

job_name = "train-ray-processing-train"

output_path = f"s3://{bucket_name}/{job_name}"

model_trainer = ModelTrainer(
training_image=image_uri,
source_code=source_code,
base_job_name=job_name,
compute=compute_configs,
hyperparameters={
"epochs": 100,
"learning_rate": 0.001,
"batch_size": 100,
},
stopping_condition=StoppingCondition(max_runtime_in_seconds=18000),
output_data_config=OutputDataConfig(
s3_output_path=output_path, compression_type="NONE"
),
role=role,
)

train_input = InputData(
channel_name="processing",
data_source=S3DataSource(
s3_data_type="S3Prefix",
s3_uri=input_data,
s3_data_distribution_type="FullyReplicated",
instance_group_names=["group_1", "group_2"]
),
)

data = [train_input]

model_trainer.train(input_data_config=data, wait=False)
```

**Expected behavior**
See Estimator behavior. `sm_drivers` and `code` are private channels configured by the SDK, so `instance_group_names` should be automatically set

**Screenshots or logs**
If applicable, add screenshots or logs to help explain your problem.

**System information**
A description of your system. Please provide:
- **SageMaker Python SDK version**: 2.247.1
- **Framework name (eg. PyTorch) or algorithm (eg. KMeans)**: Any
- **Framework version**: Any
- **Python version**: 3.12
- **CPU or GPU**: CPU and GPU
- **Custom Docker image (Y/N)**: N

**Additional context**
Add any other context about the problem here.

Commencez par exécuter la reproduction fournie de ModelTrainer avec SageMaker Python SDK 2.247.1 et examinez le point d’entrée ModelTrainer.train ainsi que les canaux d’entrée qu’il génère. Comparez la configuration générée au comportement de Estimator, en particulier les canaux privés sm_drivers et code. La tâche est terminée lorsqu’un job d’entraînement hétérogène est accepté, avec des groupes d’instances affectés de manière cohérente à chaque canal.

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