SageMaker SDK Bug Report: HyperparameterTuner Missing Container Mode Support

**Describe the bug**
HyperparameterTuner does not preserve container mode parameters (`container_entry_point` and `container_arguments`) when creating training jobs, causing tuning jobs to fail. Individual training jobs work correctly with container mode, but hyperparameter tuning jobs lose the container configuration and fall back to script mode logic, resulting in failures.

**To reproduce**
```python
from sagemaker.estimator import Estimator
from sagemaker.tuner import HyperparameterTuner, ContinuousParameter

# Create estimator with container mode
estimator = Estimator(
image_uri="123456789012.dkr.ecr.us-east-1.amazonaws.com/my-image:latest",
role="arn:aws:iam::123456789012:role/SageMakerRole",
instance_type="ml.m5.large",
instance_count=1,
container_entry_point=["python", "-m", "my_module"],
container_arguments=["train", "model1"],
)

# Create hyperparameter tuner
tuner = HyperparameterTuner(
estimator=estimator,
objective_metric_name="accuracy",
objective_type="Maximize",
hyperparameter_ranges={
"learning_rate": ContinuousParameter(0.001, 0.1)
},
max_jobs=2,
max_parallel_jobs=1,
)

# This will fail - individual training jobs missing container parameters
tuner.fit()
```

**Expected behavior**
The hyperparameter tuning job should preserve the container mode configuration and set `ContainerEntrypoint` and `ContainerArguments` in the `AlgorithmSpecification` of individual training jobs, just like when calling `estimator.fit()` directly.

**Screenshots or logs**
Individual training job within tuning job shows missing container parameters:
```json
"AlgorithmSpecification": {
"TrainingImage": "123456789012.dkr.ecr.us-east-1.amazonaws.com/my-image:latest",
"TrainingInputMode": "File",
"MetricDefinitions": [...],
"EnableSageMakerMetricsTimeSeries": false
// Missing: ContainerEntrypoint and ContainerArguments
}
```

Training jobs fail with:
```
AlgorithmError: Framework Error:
AttributeError: 'NoneType' object has no attribute 'endswith'
```

**System information**
- **SageMaker Python SDK version**: 2.244.2
- **Framework name**: Custom container (Estimator class)
- **Framework version**: N/A
- **Python version**: 3.10
- **CPU or GPU**: CPU
- **Custom Docker image (Y/N)**: Y

**Additional context**

## Root cause analysis

The issue is in two locations in the SDK:

### 1. `sagemaker/job.py` - Missing container parameter extraction

**`_Job._load_config()` method (lines 117-124)** only extracts basic configuration and ignores container mode parameters:

```python
return {
"input_config": input_config,
"role": role,
"output_config": output_config,
"resource_config": resource_config,
"stop_condition": stop_condition,
"vpc_config": vpc_config,
# Missing: container_entry_point, container_arguments
}
```

### 2. `sagemaker/session.py` - Missing container parameter handling

**`_map_training_config()` method (line 3584+)** doesn't accept container parameters in its signature and doesn't include them in the `AlgorithmSpecification` (lines 3685-3694).

The method signature is missing `container_entry_point` and `container_arguments` parameters, and the `AlgorithmSpecification` construction only includes:

```python
algorithm_spec = {"TrainingInputMode": input_mode}
if metric_definitions is not None:
algorithm_spec["MetricDefinitions"] = metric_definitions

if algorithm_arn:
algorithm_spec["AlgorithmName"] = algorithm_arn
else:
algorithm_spec["TrainingImage"] = image_uri

# Missing: ContainerEntrypoint and ContainerArguments
```

## Comparison with working code

Individual training jobs work because `session.train()` correctly handles container parameters (lines 1266-1270):

```python
if container_entry_point is not None:
train_request["AlgorithmSpecification"]["ContainerEntrypoint"] = container_entry_point

if container_arguments is not None:
train_request["AlgorithmSpecification"]["ContainerArguments"] = container_arguments
```

## Code path analysis

**Working path (individual training jobs):**
1. `estimator.fit()` → `session.train()` → ✅ **Includes container parameters**

**Broken path (hyperparameter tuning):**
1. `tuner.fit()` → `_TuningJob._prepare_training_config()`
2. → `_Job._load_config()` → ❌ **Drops container parameters**
3. → `session._map_training_config()` → ❌ **Doesn't handle container parameters**

## Verification

- ✅ Container mode works with `estimator.fit()` (individual training jobs)
- ❌ Container mode fails with `tuner.fit()` (hyperparameter tuning)
- ✅ Script mode works with `tuner.fit()`

## Impact

This prevents users from using container mode with hyperparameter tuning, forcing them to use script mode for tuning jobs even when their training logic is containerized.

## Suggested fix

1. **Update `_Job._load_config()`** to extract container parameters from the estimator:
```python
# Add to the return dict:
config = {
"input_config": input_config,
"role": role,
"output_config": output_config,
"resource_config": resource_config,
"stop_condition": stop_condition,
"vpc_config": vpc_config,
}

# Add container mode parameters
if hasattr(estimator, 'container_entry_point') and estimator.container_entry_point:
config['container_entry_point'] = estimator.container_entry_point

if hasattr(estimator, 'container_arguments') and estimator.container_arguments:
config['container_arguments'] = estimator.container_arguments

return config
```

2. **Update `_map_training_config()` signature** to accept container parameters and include them in `AlgorithmSpecification`:
```python
def _map_training_config(
cls,
static_hyperparameters,
input_mode,
role,
output_config,
stop_condition,
# ... existing params ...
container_entry_point=None, # Add this
container_arguments=None, # Add this
):
# ... existing code ...

# Add to AlgorithmSpecification:
if container_entry_point is not None:
algorithm_spec["ContainerEntrypoint"] = container_entry_point

if container_arguments is not None:
algorithm_spec["ContainerArguments"] = container_arguments
```

This would align the hyperparameter tuning code path with the working individual training job implementation.

Lee _Job._load_config() en sagemaker/job.py y _map_training_config() en sagemaker/session.py; después, sigue _TuningJob._prepare_training_config() desde tuner.fit(). Compara ese recorrido con session.train(), que el issue identifica como funcional. Se considera terminado cuando un trabajo de ajuste conserva container_entry_point y container_arguments en la AlgorithmSpecification de cada trabajo de entrenamiento y la reproducción indicada deja de fallar.

Escrito por el modelo de indexación a partir del texto del issue.