 Here is the Python code based on your instructions:

```python
import logging
import h5py
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, RandomSampler
from sklearn.model_selection import StratifiedShuffleSplit
from torchvision.transforms import Compose, RandomAffine, RandomRotation, Normalize
from scipy.ndimage import grow

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'

    # Initialize logging
    logging.basicConfig(level=logging.INFO)

    # Load configuration
    with open(config_file, 'r') as f:
        config = json.load(f)

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

    # Define batch request and snapshot request
    batch_request = {RAW_INTENSITIES: (config['batch_size'], input_size, input_size),
                     LABELLED_OBJECTS: (config['batch_size'], output_size, output_size),
                     PRE_VOXEL_AFFINITIES: (config['batch_size'], output_size, output_size),
                     LOSS_WEIGHTS: (config['batch_size'],)}
    snapshot_request = {RAW_INTENSITIES: (1, input_size, input_size),
                        LABELLED_OBJECTS: (1, output_size, output_size),
                        PRE_VOXEL_AFFINITIES: (1, output_size, output_size),
                        LOSS_WEIGHTS: (1,)}

    # Define data augmentations
    transform = Compose([RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.8, 1.2)),
                          RandomRotation(degrees=15),
                          Normalize(mean=[0.5], std=[0.5])])

    # Load data
    with h5py.File(config['data_file'], 'r') as data_file:
        raw_data = data_file[RAW_INTENSITIES][:]
        labelled_data = data_file[LABELLED_OBJECTS][:]

    # Define model, optimizer, and loss function
    model = MyModel()
    optimizer = optim.Adam(model.parameters())
    criterion = nn.BCEWithLogitsLoss()

    # Pre-cache batches
    dataloader = DataLoader(MyDataset(raw_data, labelled_data, transform, batch_size=config['batch_size']),
                             sampler=RandomSampler(len(raw_data)), batch_sampler=StridedSampler(len(raw_data), step=config['batch_stride'], drop_last=True))

    for epoch in range(iterations):
        for batch in dataloader:
            # Prepare data
            raw_intensities, labelled_objects, pre_voxel_affinities, loss_weights = map(torch.tensor, zip(*batch))

            # Normalize raw data
            raw_intensities = raw_intensities / 255.0 - 0.5

            # Choose random location
            indices = torch.randint(len(raw_intensities), (1,))
            raw_intensities, labelled_objects, pre_voxel_affinities, loss_weights = raw_intensities[indices], labelled_objects[indices], pre_voxel_affinities[indices], loss_weights[indices]

            # Apply augmentations
            raw_intensities = transform(raw_intensities)
            labelled_objects = transform(labelled_objects)

            # Grow boundary between labels
            labelled_objects_with_boundary = grow_label(labelled_objects, config['boundary_size'])

            # Convert labels into affinities
            pre_voxel_affinities = convert_labels_to_affinities(labelled_objects_with_boundary, output_size)

            # Balance labels
            pre_voxel_affinities, loss_weights = balance_labels(pre_voxel_affinities, loss_weights)

            # Forward pass and backpropagation
            predictions = model(raw_intensities)
            loss = criterion(predictions, pre_voxel_affinities)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            # Save batch for inspection
            snapshot_data = {RAW_INTENSITIES: raw_intensities,
                             LABELLED_OBJECTS: labelled_objects,
                             PRE_VOXEL_AFFINITIES: pre_voxel_affinities,
                             LOSS_WEIGHTS: loss_weights,
                             PREDICTED_AFFINITIES: predictions,
                             GRADIENTS: predictions.grad}
            with h5py.File(f'snapshot_{epoch}.h5', 'w') as snapshot_file:
                for key, value in snapshot_data.items():
                    snapshot_file.create_dataset(key, data=value.numpy())

            # Print profiling stats
            logger.info(f'Epoch: {epoch + 1}, Batch: {dataloader.current_index}, Loss: {loss.item()}')

    logger.info('Training complete.')

if __name__ == '__main__':
    train_model(100, 'config.json')
```

This code assumes the existence of a custom `MyModel` class, `MyDataset` class, `grow_label` function, `convert_labels_to_affinities` function, and `balance_labels` function. You will need to implement these functions according to your specific requirements.