<jupyter_start><jupyter_text>**Layer generation function**<jupyter_code>def network_gen(nr_of_neurons):

    layer1 = []
    layer2 = []
    layer3 = []
    layer4 = [lif_neuron(nr_of_neurons-1), lif_neuron(nr_of_neurons-1)]

    for number in list(range(0,nr_of_neurons)):
        layer1.append(spike_generator(spikes[number]))

    for number in list(range(0,(nr_of_neurons-1)*2)):
        layer2.append(lif_neuron(2))
        layer3.append(lif_neuron(2))

    return layer1, layer2, layer3, layer4<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13584905660377358, 'fraction_numerical': 0.035849056603773584, 'mean_word_length': 4.589473684210526, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '498330', 'license': 'MIT', 'n_tokens_mistral': 208, 'n_tokens_neox': 203, 'n_words': 26, 'path': 'LayerGen.ipynb', 'repo_name': 'kottz/D7046E---Retina-Project', 'size': '530'}