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.

Start by running the provided ModelTrainer reproduction with SageMaker Python SDK 2.247.1 and inspect the ModelTrainer.train entry point and its generated input channels. Compare the generated configuration with Estimator behavior, especially the private sm_drivers and code channels. Done means a heterogeneous training job is accepted with instance groups assigned consistently to every channel.

Written by the indexing model from the issue text.