```python
import logging
import json
from some_deep_learning_library import (request_batch, BatchRequest, test_up_and_downsample, shift_and_crop, 
                                        test_jitter_and_random, AddAffinities, BatchRequestError, 
                                        test_shift_and_crop2, test_mirror_and_transpose, test_shift_and_crop1, 
                                        test_shift_and_crop4, test_shift_and_crop3, SpecifiedLocation, 
                                        PrintProfilingStats, __read_file, Batch, _add_to_batch, 
                                        AddNonsymmetricAffinities, PipelineRequestError, test_shift_and_crop_static)

# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')

def train_model(iterations):
    logging.info("Starting training...")

    # Array keys
    raw_intensities = "raw_intensities"
    labelled_objects = "labelled_objects"
    per_voxel_affinities = "per_voxel_affinities"
    loss_weights = "loss_weights"
    predicted_affinities = "predicted_affinities"
    gradients = "gradients"

    # Read configuration
    with open('config.json', 'r') as config_file:
        config = json.load(config_file)
    input_size = config['input_size']
    output_size = config['output_size']

    # Formulate batch and snapshot requests
    batch_request = BatchRequest()
    snapshot_request = BatchRequest()

    # Assemble training pipeline
    pipeline = (
        request_batch(raw_intensities, labelled_objects)
        + __read_file('data.hdf5', raw_intensities, labelled_objects)
        + test_up_and_downsample(raw_intensities, factor=2)
        + shift_and_crop(raw_intensities, shift=2)
        + test_jitter_and_random(raw_intensities)
        + test_mirror_and_transpose(raw_intensities)
        + test_shift_and_crop1(raw_intensities)
        + test_shift_and_crop2(raw_intensities)
        + test_shift_and_crop3(raw_intensities)
        + test_shift_and_crop4(raw_intensities)
        + test_shift_and_crop_static(raw_intensities)
        + SpecifiedLocation()
        + AddAffinities(labelled_objects, per_voxel_affinities)
        + AddNonsymmetricAffinities(labelled_objects, per_voxel_affinities)
        + _add_to_batch(loss_weights)
        + PrintProfilingStats()
    )

    # Train for specified number of iterations
    for i in range(iterations):
        try:
            batch = pipeline.request_batch(batch_request)
            # Simulate training iteration
            logging.info(f"Training iteration {i+1}/{iterations}")
        except (BatchRequestError, PipelineRequestError) as e:
            logging.error(f"Error during batch request: {e}")
            break

    logging.info("Training completed.")

if __name__ == "__main__":
    num_iterations = 100  # Specify the number of iterations
    train_model(num_iterations)
```