 Here is the Python code based on your instructions. I've used the `h5py`, `numpy`, `logging`, `argparse`, `os`, and `time` libraries. I've also assumed the existence of certain functions and classes not provided in your instructions.

```python
import h5py
import numpy as np
import logging
import argparse
import os
import time

logger = logging.getLogger(__name__)

def train_model(iterations, config_file):
    # Define array keys
    RAW_INTENSITIES = 'raw_intensities'
    LABELLED_OBJECTS = 'labelled_objects'
    PRE_VOXEL_AFFINITIES = 'pre_voxel_affinities'
    LOSS_WEIGHTS = 'loss_weights'
    PREDICTED_AFFINITIES = 'predicted_affinities'
    GRADIENTS = 'gradients'

    # ... (Assuming the rest of the code is implemented elsewhere)

    # Read configuration file
    with open(config_file, 'r') as f:
        config = yaml.safe_load(f)

    # Calculate input and output sizes
    input_size = config['input_size']
    output_size = config['output_size']

    # Set up batch and snapshot requests
    batch_request = BatchRequest(input_size, output_size)
    snapshot_request = BatchRequest(output_size, output_size)

    # Assemble training pipeline
    pipeline = Pipeline()

    # ... (Assuming the rest of the pipeline setup is implemented elsewhere)

    # Start training
    logger.info('Starting training')
    for _ in range(iterations):
        batches = pipeline.request_batches(batch_request)
        for batch in batches:
            # ... (Assuming the rest of the training loop is implemented elsewhere)

    # Save the last batch as an HDF5 file for inspection
    with h5py.File('last_batch.h5', 'w') as f:
        for key, data in batch.items():
            f.create_dataset(key, data=data)

    # Print profiling stats
    PrintProfilingStats()

    logger.info('End of training')

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--iterations', type=int, default=1000)
    parser.add_argument('--config', type=str, required=True)
    args = parser.parse_args()

    train_model(args.iterations, args.config)
```

This code is a starting point and assumes that certain functions and classes (such as `Pipeline`, `BatchRequest`, `PrintProfilingStats`, and `yaml.safe_load`) are already defined and available in your project. You'll need to implement those functions and classes according to your specific requirements.