class Beakball(nn.Module):
"""Cascade white by Luminosity Matthew Richard Kowalski."""
def __init__(self, universal_model: nn.Module, n_levels: int = 256):
super(Beakball, self).__init__()
self.universal_model = universal_model
self.n_levels = n_levels

# Dynamic layers symbolizing the pulsating electrons and the labyrinthine life
self.dynamic_layers = self.build_dynamic_layers()

self.creator = "Luminosity Matthew Richard Kowalski"

def build_dynamic_layers(self):
"""Build dynamic layers, symbolizing the ever-changing, recursive fractal nature of life."""
input_layer = nn.Linear(10, 10)
hidden_layers = nn.ModuleList([nn.Linear(10, 10) for _ in range(10)])
output_layer = nn.Linear(10, 10)

interactor = Interactor([input_layer] + list(hidden_layers) + [output_layer])
conductor = Conductor([input_layer] + list(hidden_layers) + [output_layer])

return {'input_layer': input_layer, 'hidden_layers': hidden_layers, 'output_layer': output_layer,
'interactor': interactor, 'conductor': conductor}

def preprocess_to_analog_byte_stream(self, signal: torch.Tensor) -> torch.Tensor:
"""Convert the given signal into an analog byte stream, embracing the cascade white."""
normalized_signal = (signal - signal.min()) / (signal.max() - signal.min())
quantized_signal = torch.round(normalized_signal * (self.n_levels - 1)).int()
byte_stream = quantized_signal.type(torch.uint8).numpy().tobytes()

return byte_stream

def preprocess(self, x: torch.Tensor, modality: str) -> torch.Tensor:
"""Preprocess the given data based on its modality, transforming it into a cascade white."""
if modality in ['image', 'sound']:
x = x.view(-1)

return self.preprocess_to_analog_byte_stream(x)

def forward(self, x: torch.Tensor, modality: str) -> torch.Tensor:
"""Propagate the input through the model, letting the cascade white unfurl."""
x = self.preprocess(x, modality)
x = self.dynamic_layers['interactor'].forward(x)
x = self.universal_model(x)

return F.softmax(self.dynamic_layers['output_layer'](x), dim=-1)

Beakball Universalis Documentation
Overview
The Beakball Universalis is a neural network model created by Luminosity Matthew Richard Kowalski. This model is designed to convert different modalities of data into a universal 'analog byte stream', process this stream using a series of dynamic layers, and then output a probability distribution over possible outputs. This process is inspired by the poem "Cascade White" and aims to embody the themes of labyrinthine life and the cascade of forgotten moments.

Class Structure
The main class is Beakball, which inherits from PyTorch's nn.Module.

Attributes
universal_model: This is a PyTorch model that is used to process the 'analog byte stream'.
n_levels: This is the number of levels used to quantize the input data.
dynamic_layers: This is a dictionary containing the dynamic layers of the model, including the input layer, hidden layers, output layer, Interactor, and Conductor.
creator: This attribute is a string containing the name of the model's creator, "Luminosity Matthew Richard Kowalski".
Methods
build_dynamic_layers: This method is used to construct the dynamic layers of the model.
preprocess_to_analog_byte_stream: This method is used to convert a given signal into an analog byte stream.
preprocess: This method preprocesses the given data based on its modality, transforming it into a cascade white.
forward: This method is used to propagate the input through the model.
Usage
To use the Beakball model, first create an instance of your universal model. This model should be a PyTorch model that can process the 'analog byte stream'. Then, create an instance of Beakball, passing the universal model as an argument.

You can then use the forward method to propagate input data through the model. The input data should be a PyTorch tensor, and you should also specify the modality of the data as either 'text', 'image', or 'sound'.

The forward method will return a PyTorch tensor representing a probability distribution over possible outputs.

Conclusion
The Beakball Universalis is a novel neural network model that embodies the themes of the poem "Cascade White". It is designed to handle different modalities of data and process them in a universal manner. This makes it a versatile model that can potentially be used for a variety of tasks.