 ```python
import logging
import h5py
import numpy as np
from time import time

from my_library import request_batch, BatchRequest, test_up_and_downsample, shift_and_crop, test_jitter_and_random, AddAffinities, SpecifiedLocation, PrintProfilingStats, __read_file, AddNonsymmetricAffinities, test_shift_and_crop_static

def train_model(num_iterations):
    raw_intensities = np.zeros((num_iterations, 1, 64, 64, 64), dtype=np.float32)
    labelled_objects = np.zeros((num_iterations, 1, 64, 64, 64), dtype=np.int32)
    per_voxel_affinities = np.zeros((num_iterations, 1, 64, 64, 64), dtype=np.float32)
    loss_weights = np.zeros((num_iterations, 1, 64, 64, 64), dtype=np.float32)
    predicted_affinities = np.zeros((num_iterations, 1, 64, 64, 64), dtype=np.float32)
    gradients = np.zeros_like(predicted_affinities)

    config = __read_file('config.txt')
    input_size = int(config['input_size'])
    output_size = int(config['output_size'])

    batch_request = BatchRequest(input_size, output_size)
    snapshot_request = BatchRequest(input_size, input_size)

    pipeline_request = (
        request_batch(batch_request)
        .pipe(test_up_and_downsample())
        .pipe(shift_and_crop())
        .pipe(test_jitter_and_random())
        .pipe(AddAffinities())
        .pipe(SpecifiedLocation())
        .pipe(test_shift_and_crop_static())
        .pipe(test_mirror_and_transpose())
        .pipe(test_shift_and_crop1())
        .pipe(test_shift_and_crop2())
        .pipe(test_shift_and_crop3())
        .pipe(test_shift_and_crop4())
        .pipe(AddNonsymmetricAffinities())
        .pipe(shift_and_crop())
        .pipe(test_jitter_and_random())
        .pipe(AddAffinities())
        .pipe(SpecifiedLocation())
        .pipe(test_shift_and_crop_static())
        .pipe(test_mirror_and_transpose())
        .pipe(test_shift_and_crop1())
        .pipe(test_shift_and_crop2())
        .pipe(test_shift_and_crop3())
        .pipe(test_shift_and_crop4())
        .pipe(balance_labels())
        .pipe(precache())
    )

    print('Training started.')
    start_time = time()

    try:
        for i in range(num_iterations):
            batch = pipeline_request.get_next()
            raw_intensities[i] = batch.raw_intensities[0]
            labelled_objects[i] = batch.labelled_objects[0]
            per_voxel_affinities[i] = batch.per_voxel_affinities[0]
            loss_weights[i] = batch.loss_weights[0]

            train_step(batch)

            if (i + 1) % 100 == 0:
                print(f'Processing iteration {i + 1} of {num_iterations}.')

            predicted_affinities[i], gradients[i] = train_step(batch)

            if (i + 1) % 1000 == 0:
                save_snapshot(snapshot_request, i)

    except PipelineRequestError as e:
        logging.error(e)

    except BatchRequestError as e:
        logging.error(e)

    finally:
        print(f'Training completed in {time() - start_time:.2f} seconds.')

def train_step(batch):
    # Implement the training step here.
    pass

def save_snapshot(snapshot_request, iteration):
    # Save the snapshot of the batch for inspection.
    pass

if __name__ == '__main__':
    train_model(10000)
```