Windows host writes sm_train.sh with CRLF in SDK v3, causing SageMaker training job bootstrap failure ($'\r': command not found)

**PySDK Version**
- [ ] PySDK V2 (2.x)
- [x] PySDK V3 (3.x)

**Describe the bug**
When launching a SageMaker training job from Windows using `sagemaker.train.ModelTrainer` (SDK v3), the generated bootstrap script `sm_train.sh` is written with CRLF line endings. Inside the Linux training container, bash fails to parse it and the job exits before user training code starts.

The script appears to be written in `sagemaker/train/model_trainer.py` with:

`with open(os.path.join(tmp_dir.name, TRAIN_SCRIPT), "w") as f:`

which applies platform newline conversion on Windows (`\r\n`).

**To reproduce**
1. Use Windows host with SageMaker Python SDK v3.
2. Run this complete script (replace `ROLE_ARN` and `S3_INPUT`):

```python
from sagemaker.core import image_uris
from sagemaker.core.helper.session_helper import Session
from sagemaker.core.training.configs import SourceCode, Compute, InputData
from sagemaker.train import ModelTrainer

session = Session()
region = session.boto_region_name

training_image = image_uris.retrieve(
framework="pytorch",
region=region,
version="2.4.0",
py_version="py311",
instance_type="ml.g4dn.xlarge",
image_scope="training",
)

trainer = ModelTrainer(
sagemaker_session=session,
role="ROLE_ARN",
training_image=training_image,
source_code=SourceCode(
source_dir=".",
entry_script="sagemaker_entry.py",
requirements="requirements/sagemaker_train.txt",
),
compute=Compute(instance_type="ml.g4dn.xlarge", instance_count=1),
)

trainer.train(
input_data_config=[InputData(channel_name="train", data_source="S3_INPUT")],
wait=True,
logs=True,
)
```

3. Check CloudWatch logs for the training job.

**Expected behavior**
`sm_train.sh` should be written with LF (`\n`) and execute correctly in the Linux container, allowing the training entry point to start.

**Screenshots or logs**
Observed logs:

```text
/opt/ml/input/data/sm_drivers/sm_train.sh: line 1: $'\r': command not found
Starting training script#015
/opt/ml/input/data/sm_drivers/sm_train.sh: line 3: set: -#015: invalid option
set: usage: set [-abefhkmnptuvxBCHP] [-o option-name] [--]
/opt/ml/input/data/sm_drivers/sm_train.sh: line 5: $'\r': command not found
/opt/ml/input/data/sm_drivers/sm_train.sh: line 6: syntax error near unexpected token `$'{\r''
/opt/ml/input/data/sm_drivers/sm_train.sh: line 6: `handle_error() {#015'
```

**System information**
- **SageMaker Python SDK version**: 3.12.0
- **Framework name (eg. PyTorch) or algorithm (eg. KMeans)**: PyTorch
- **Framework version**: 2.4.0 (DLC)
- **Python version**: 3.11
- **CPU or GPU**: GPU (`ml.g4dn.xlarge`)
- **Custom Docker image (Y/N)**: N

**Additional context**
- Reproduced from Windows 10 host.
- Workaround: launching from Linux (WSL/Studio/EC2) avoids CRLF in `sm_train.sh`.
- Suggested fix in SDK: force LF on script write, e.g. `open(..., "w", newline="\n")`.

Examinez le code d’écriture du script dans sagemaker/train/model_trainer.py, en particulier l’appel open montré dans l’issue, et reproduisez le fichier généré sous Windows. C’est terminé lorsque sm_train.sh contient des fins de ligne LF et que les journaux du conteneur SageMaker montrent que le point d’entrée de l’entraînement démarre sans les erreurs d’analyse bash signalées.

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