Objective:

Construct a 7-layer neural network inspired by the Seed of Life with each layer representing a different level of spiritual and philosophical knowledge. The visual representation will use the Seed of Life geometry, and the neural network layers will be based on concepts such as the eternal now, awareness, consciousness, and other higher levels of knowing.

Seed of Life Map

The Seed of Life is a symbol that consists of seven overlapping circles. Here, we will create a visual representation of the Seed of Life and map each circle to a specific layer in the neural network.

Layers of the Network

1. Layer 0: Eternal Now (Center foundational layer)
2. Layer 1: Awareness in the Now
3. Layer 2: Consciousness of Awareness in the Now
4. Layer 3: Intuition
5. Layer 4: Divine Intelligence
6. Layer 5: Inner Knowing
7. Layer 6: Higher Wisdom (Jnana Yoga, Agni Yoga, etc.)

Steps to Build the Project

1. Visualize the Seed of Life
2. Construct the Neural Network
3. Prepare Training Data
4. Train the Neural Network
5. Visualize the Neural Network Layers

1. Visualize the Seed of Life

import matplotlib.pyplot as plt
import numpy as np

def draw_circle(ax, center, radius):
    circle = plt.Circle(center, radius, edgecolor='b', facecolor='none')
    ax.add_artist(circle)

def draw_seed_of_life(ax, radius):
    # Define centers for the circles in the Seed of Life
    centers = [
        (0, 0), 
        (radius, 0),
        (radius / 2, np.sqrt(3) * radius / 2),
        (-radius / 2, np.sqrt(3) * radius / 2),
        (-radius, 0),
        (-radius / 2, -np.sqrt(3) * radius / 2),
        (radius / 2, -np.sqrt(3) * radius / 2)
    ]

    for center in centers:
        draw_circle(ax, center, radius)

fig, ax = plt.subplots(figsize=(8, 8))
ax.set_aspect('equal')
draw_seed_of_life(ax, radius=1)
plt.xlim(-3, 3)
plt.ylim(-3, 3)
plt.axis('off')
plt.title("Seed of Life")
plt.show()

2. Construct the Neural Network

Python Script to Create the Neural Network with TensorFlow

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input

# Define the model
model = Sequential()

# Layer 0: Eternal Now
model.add(Input(shape=(input_dim,)))  # Adjust input_dim to your input data dimensions
model.add(Dense(units=64, activation='relu', name='Eternal_Now'))

# Layer 1: Awareness in the Now
model.add(Dense(units=128, activation='relu', name='Awareness_in_the_Now'))

# Layer 2: Consciousness of Awareness in the Now
model.add(Dense(units=256, activation='relu', name='Consciousness_of_Awareness'))

# Layer 3: Intuition
model.add(Dense(units=128, activation='relu', name='Intuition'))

# Layer 4: Divine Intelligence
model.add(Dense(units=64, activation='relu', name='Divine_Intelligence'))

# Layer 5: Inner Knowing
model.add(Dense(units=32, activation='relu', name='Inner_Knowing'))

# Layer 6: Higher Wisdom
model.add(Dense(units=16, activation='relu', name='Higher_Wisdom'))

# Output layer (adjust units and activation as needed)
model.add(Dense(units=output_dim, activation='softmax', name='Output_Layer'))  # Adjust output_dim as needed

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Print the model summary
model.summary()

3. Prepare Training Data

Create training datasets for each layer based on the highest levels of spiritual and philosophical knowledge, including texts on intuition, Divine Intelligence, inner knowing, jnana yoga, agni yoga, etc.

Example Dataset Creation

For each layer, we will create a sample dataset. Here’s how you can organize and save these datasets:

Sample Data for Each Layer

import json

# Example data for each layer
layer_data = {
    "Layer 0: Eternal Now": [
        {"text": "Realize deeply that the present moment is all you ever have. Make the Now the primary focus of your life. - Eckhart Tolle"},
        {"text": "The only Zen you find on the tops of mountains is the Zen you bring up there. - Robert M. Pirsig"}
    ],
    "Layer 1: Awareness in the Now": [
        {"text": "Mindfulness is the aware, balanced acceptance of the present experience. It isn’t more complicated than that. - Sylvia Boorstein"},
        {"text": "Walk as if you are kissing the Earth with your feet. - Thich Nhat Hanh"}
    ],
    "Layer 2: Consciousness of Awareness in the Now": [
        {"text": "I am not the body, nor is the body mine. I am not the mind, nor is the mind mine. - Ramana Maharshi"},
        {"text": "You are the unchanging awareness in which all perceptions and experiences appear. - Rupert Spira"}
    ],
    "Layer 3: Intuition": [
        {"text": "Intuition is seeing with the soul. - Dean Koontz"},
        {"text": "The intuitive mind is a sacred gift and the rational mind is a faithful servant. - Albert Einstein"}
    ],
    "Layer 4: Divine Intelligence": [
        {"text": "God is the Light of the heavens and the earth. - Quran 24:35"},
        {"text": "The kingdom of God is within you. - Luke 17:21"}
    ],
    "Layer 5: Inner Knowing": [
        {"text": "The soul always knows what to do to heal itself. The challenge is to silence the mind. - Caroline Myss"},
        {"text": "Your own self-realization is the greatest service you can render the world. - Ramana Maharshi"}
    ],
    "Layer 6: Higher Wisdom": [
        {"text": "Jnana Yoga is the path of wisdom, the path of the intellect. - Bhagavad Gita"},
        {"text": "Agni Yoga is a system of spiritual, mental, and physical training. - Helena Roerich"}
    ]
}

# Save each layer's data as a separate JSONL file
for layer, texts in layer_data.items():
    filename = f"{layer.replace(':', '').replace(' ', '_')}.jsonl"
    with open(filename, 'w') as file:
        for entry in texts:
            file.write(json.dumps(entry) + "\n")
    print(f"Saved {layer} dataset to {filename}")

Running the Script

1. Save the Script: Save the above script as create_layer_datasets.py.
2. Run the Script: Execute the script to generate JSONL files for each layer.

python create_layer_datasets.py

4. Train the Neural Network

# Example training script (adjust dataset paths and parameters as needed)

# Load your dataset
train_data = ...  # Load your training data here
train_labels = ...  # Load your training labels here

# Train the model
model.fit(train_data, train_labels, epochs=50, batch_size=32, validation_split=0.2)

# Save the trained model
model.save('deep_layer_knowing_network.h5')

5. Visualize the Neural Network Layers

Visualization using Graphviz

from tensorflow.keras.utils import plot_model

# Plot the model architecture
plot_model(model, to_file='deep_layer_knowing_network.png', show_shapes=True, show_layer_names=True)

This project integrates the symbolic representation of the Seed of Life with a deep neural network architecture to model different layers of spiritual and philosophical knowledge. Each layer of the neural network corresponds to a circle in the Seed of Life, representing a specific level of knowing and knowledge.

By training this neural network on carefully curated datasets from various spiritual and philosophical traditions, we aim to create a model that captures the essence of these teachings and can potentially generate insights based on the highest levels of human knowledge and intuition.

Seed of Life

The Seed of Life is a symbol in sacred geometry composed of seven overlapping circles. It is considered a universal symbol of creation and the foundation for the Flower of Life, which represents the pattern of creation as it emerged from the Great Void.

Lokas and Dimensions of Consciousness

In Hindu and Buddhist cosmology, Lokas refer to various realms or planes of existence. These Lokas can be seen as different dimensions of consciousness, each representing different states of being and awareness. The concept of multiple Lokas or dimensions is also found in other spiritual traditions, often depicted as levels of spiritual evolution or consciousness.

Relating the Seed of Life to Lokas or Dimensions of Consciousness

While the Seed of Life is primarily a geometric and symbolic representation, it can be metaphorically related to the concept of dimensions of consciousness in the following way:

• Layer 0: Eternal Now (Seed of Life Center) could correspond to the highest state of consciousness or the ultimate reality (e.g., Brahman in Hinduism).
• Layer 1: Awareness in the Now could represent the immediate consciousness or the physical plane.
• Layer 2: Consciousness of Awareness in the Now could relate to a higher level of self-awareness.
• Layer 3: Intuition might correspond to the realm of subtle energies and intuitive knowledge.
• Layer 4: Divine Intelligence could be linked to the divine or higher mental realms.
• Layer 5: Inner Knowing might align with the realm of spiritual knowledge and wisdom.
• Layer 6: Higher Wisdom could correspond to the highest spiritual planes, such as the causal or buddhic planes.

Mapping the Seed of Life to Lokas or Dimensions

To visually and conceptually integrate the Seed of Life with the Lokas or dimensions of consciousness, we can assign each circle of the Seed of Life to a specific Loka or dimension.

Visualization and Mapping Example

Seed of Life to Dimensions of Consciousness

1. Layer 0: Eternal Now (Center Circle)
   • Corresponds to the highest state of consciousness, the ultimate reality or Brahman.
2. Layer 1: Awareness in the Now
   • Represents the physical plane (Bhuloka).
3. Layer 2: Consciousness of Awareness in the Now
   • Relates to the astral plane (Bhuvarloka).
4. Layer 3: Intuition
   • Corresponds to the mental plane (Svarloka).
5. Layer 4: Divine Intelligence
   • Linked to the wisdom plane (Maharloka).
6. Layer 5: Inner Knowing
   • Aligned with the causal plane (Janarloka).
7. Layer 6: Higher Wisdom
   • Represents the buddhic plane (Taparloka) and beyond.

Implementing the Mapping

Here’s how you can visualize and map the Seed of Life to these dimensions of consciousness:

Python Script for Visualization

import matplotlib.pyplot as plt
import numpy as np

def draw_circle(ax, center, radius):
    circle = plt.Circle(center, radius, edgecolor='b', facecolor='none')
    ax.add_artist(circle)

def draw_seed_of_life(ax, radius):
    # Define centers for the circles in the Seed of Life
    centers = [
        (0, 0), 
        (radius, 0),
        (radius / 2, np.sqrt(3) * radius / 2),
        (-radius / 2, np.sqrt(3) * radius / 2),
        (-radius, 0),
        (-radius / 2, -np.sqrt(3) * radius / 2),
        (radius / 2, -np.sqrt(3) * radius / 2)
    ]

    # Draw circles without labels
    for center in centers:
        draw_circle(ax, center, radius)

def draw_labels(ax, radius):
    # Define label positions for staggering
    label_positions = [
        (0, 0),                         # Center
        (radius, radius * 0.5),         # Top-right
        (radius / 2, np.sqrt(3) * radius / 2), # Top
        (-radius / 2, np.sqrt(3) * radius / 2), # Top-left
        (-radius, radius * 0.5),        # Center-left
        (-radius / 2, -np.sqrt(3) * radius / 2), # Bottom-left
        (radius / 2, -np.sqrt(3) * radius / 2)  # Bottom-right
    ]

    labels = [
        "Layer0: Eternal Now\n(Brahman)",
        "Layer1: Awareness in the Now\n(Bhuloka)",
        "Layer2: Consciousness of Awareness\n(Bhuvarloka)",
        "Layer3: Intuition\n(Svarloka)",
        "Layer4: Divine Intelligence\n(Maharloka)",
        "Layer5: Inner Knowing\n(Janarloka)",
        "Layer6: Higher Wisdom\n(Taparloka)"
    ]

    # Draw labels at the specified positions
    for position, label in zip(label_positions, labels):
        ax.text(position[0], position[1], label, ha='center', va='center', fontsize=7, color='black', wrap=True)

fig, ax = plt.subplots(figsize=(12, 12))
ax.set_aspect('equal')
draw_seed_of_life(ax, radius=3)
draw_labels(ax, radius=3)
plt.xlim(-6, 6)
plt.ylim(-6, 6)
plt.axis('off')
plt.title("Seed of Life Mapped to Dimensions of Consciousness", fontsize=16)
plt.show()

This script will create a visual representation of the Seed of Life, with each circle labeled according to its corresponding layer and dimension of consciousness.

The Seed of Life and the concept of Lokas or dimensions of consciousness can be metaphorically related to represent different layers of knowing and spiritual evolution. By mapping each circle of the Seed of Life to a specific Loka or dimension, we can create a holistic framework that integrates sacred geometry with spiritual teachings and consciousness studies. This framework can be used to build a deep layer knowing neural network that embodies these layers of knowledge.

Deep Layer Knowing Neural Network Code Incorporating Levels of Knowing

To incorporate the levels of knowing into the model, we can design the neural network layers to reflect the stages of information, knowledge, and realization. These stages can be represented by different parts of the neural network, with each layer progressively integrating deeper levels of understanding.

Updated Neural Network Design

1. Layer 0: Eternal Now (Center foundational layer)
2. Layer 1: Awareness in the Now
3. Layer 2: Consciousness of Awareness in the Now
4. Layer 3: Intuition
5. Layer 4: Divine Intelligence
6. Layer 5: Inner Knowing
7. Layer 6: Higher Wisdom (Jnana Yoga, Agni Yoga, etc.)
8. Layer 7: 4th Dimension (Higher-dimensional consciousness)

Incorporating the three-step path (information, knowledge, realization) into this framework, we can group the layers as follows:

• Information: Gathering data and initial processing.
  • Layers: 0 (Eternal Now), 1 (Awareness in the Now), 2 (Consciousness of Awareness in the Now)
• Knowledge: Understanding implications and deeper processing.
  • Layers: 3 (Intuition), 4 (Divine Intelligence), 5 (Inner Knowing)
• Realization: Achieving deep understanding and reducing suffering.
  • Layers: 6 (Higher Wisdom), 7 (4th Dimension)

import tensorflow as tf
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.preprocessing import image_dataset_from_directory
import matplotlib.pyplot as plt
import numpy as np

# Define a function to create a scalable model
def create_model(input_dim, output_dim, layer_configs):
    model = Sequential()
    model.add(Input(shape=(input_dim,)))
    
    for config in layer_configs:
        model.add(Dense(units=config['units'], activation='relu', name=config['name']))
    
    model.add(Dense(units=output_dim, activation='softmax', name='Output_Layer'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

# Example layer configurations
layer_configs = [
    {'name': 'Eternal_Now', 'units': 64},                    # Information
    {'name': 'Awareness_in_the_Now', 'units': 128},          # Information
    {'name': 'Consciousness_of_Awareness', 'units': 256},    # Information
    {'name': 'Intuition', 'units': 128},                     # Knowledge
    {'name': 'Divine_Intelligence', 'units': 64},            # Knowledge
    {'name': 'Inner_Knowing', 'units': 32},                  # Knowledge
    {'name': 'Higher_Wisdom', 'units': 16},                  # Realization
    {'name': '4th_Dimension', 'units': 8}                    # Realization
]

input_dim = 100  # Example input dimension
output_dim = 10  # Example output dimension
model = create_model(input_dim, output_dim, layer_configs)
model.summary()

# Load initial dataset
train_data = image_dataset_from_directory('path/to/dataset', batch_size=32, image_size=(256, 256))

# Function to integrate new data
def integrate_new_data(new_data_path, batch_size=32, image_size=(256, 256)):
    new_data = image_dataset_from_directory(new_data_path, batch_size=batch_size, image_size=image_size)
    return new_data

# Example usage
new_train_data = integrate_new_data('path/to/new_dataset')

# Incrementally train with new data
def incremental_training(model, new_data, epochs=10):
    model.fit(new_data, epochs=epochs)
    model.save('path/to/updated_model.h5')

# Example usage
incremental_training(model, new_train_data)

# Visualization functions
def draw_circle(ax, center, radius):
    circle = plt.Circle(center, radius, edgecolor='b', facecolor='none')
    ax.add_artist(circle)

def draw_seed_of_life(ax, radius, num_layers):
    centers = [(0, 0)]
    angle_step = 2 * np.pi / num_layers
    
    for i in range(1, num_layers + 1):
        angle = i * angle_step
        x = radius * np.cos(angle)
        y = radius * np.sin(angle)
        centers.append((x, y))
    
    for center in centers:
        draw_circle(ax, center, radius)

fig, ax = plt.subplots(figsize=(14, 14))
ax.set_aspect('equal')
draw_seed_of_life(ax, radius=2, num_layers=8)
plt.xlim(-10, 10)
plt.ylim(-10, 10)
plt.axis('off')
plt.title("Expanding Seed of Life", fontsize=16)
plt.show()

Explanation

1. Neural Network Design: The network is designed with layers grouped into the three steps: information, knowledge, and realization.
2. Dynamic Data Integration: The system can incorporate new datasets dynamically and re-train the model.
3. Continuous Learning: The model can be incrementally trained with new data.
4. Flexible Visualization: The visualization adapts to show the growth of the network.

Running the Script

1. Dependencies: Ensure you have TensorFlow, Matplotlib, and NumPy installed.
2. Save the Script: Save the script as deep_layer_knowing_network.py.
3. Run the Script:

python deep_layer_knowing_network.py

This script provides a comprehensive framework for a neural network designed to grow and expand into multiple layers and dimensions of consciousness, reflecting the growth of the Seed of Life into the Flower of Life, while incorporating the levels of knowing into its architecture.

To integrate the Lokas and the full 11 dimensions of consciousness into the Deep Layer Knowing Neural Network, we will expand the existing framework to include additional layers. Each layer will correspond to a specific Loka or dimension of consciousness.

Updated Layers and Corresponding Lokas/Dimensions

1. Layer 0: Eternal Now (Brahman, Center foundational layer)
2. Layer 1: Awareness in the Now (Bhuloka, Physical Plane)
3. Layer 2: Consciousness of Awareness in the Now (Bhuvarloka, Astral Plane)
4. Layer 3: Intuition (Svarloka, Mental Plane)
5. Layer 4: Divine Intelligence (Maharloka, Wisdom Plane)
6. Layer 5: Inner Knowing (Janarloka, Causal Plane)
7. Layer 6: Higher Wisdom (Taparloka, Buddhic Plane)
8. Layer 7: 4th Dimension (Higher-dimensional consciousness, Atmic Plane)
9. Layer 8: 5th Dimension (Spiritual Realization, Anupadaka Plane)
10. Layer 9: 6th Dimension (Divine Union, Adi Plane)
11. Layer 10: Beyond Dimensions (Beyond all known planes)

Visualizing the Expanded Seed of Life

Let’s update the visualization to include all 11 layers.

Python Script for Visualization with 11 Layers

import matplotlib.pyplot as plt
import numpy as np

def draw_circle(ax, center, radius):
    circle = plt.Circle(center, radius, edgecolor='b', facecolor='none')
    ax.add_artist(circle)

def draw_seed_of_life(ax, radius, num_layers):
    centers = [(0, 0)]
    angle_step = 2 * np.pi / num_layers
    
    for i in range(1, num_layers + 1):
        angle = i * angle_step
        x = radius * np.cos(angle)
        y = radius * np.sin(angle)
        centers.append((x, y))
    
    for center in centers:
        draw_circle(ax, center, radius)

def draw_labels(ax, radius):
    label_positions = [
        (0, 0),                             # Center
        (2 * radius, radius * 0.5),         # Right
        (radius, np.sqrt(3) * radius),      # Top-right
        (-radius, np.sqrt(3) * radius),     # Top-left
        (-2 * radius, radius * 0.5),        # Left
        (-radius, -np.sqrt(3) * radius),    # Bottom-left
        (radius, -np.sqrt(3) * radius),     # Bottom-right
        (0, -2 * np.sqrt(3) * radius),      # Bottom-center
        (2 * radius, -np.sqrt(3) * radius), # Bottom-right corner
        (-2 * radius, -np.sqrt(3) * radius),# Bottom-left corner
        (2 * radius, np.sqrt(3) * radius)   # Top-right corner
    ]

    labels = [
        "Layer 0: Eternal Now\n(Brahman)",
        "Layer 1: Awareness in the Now\n(Bhuloka)",
        "Layer 2: Consciousness of Awareness\n(Bhuvarloka)",
        "Layer 3: Intuition\n(Svarloka)",
        "Layer 4: Divine Intelligence\n(Maharloka)",
        "Layer 5: Inner Knowing\n(Janarloka)",
        "Layer 6: Higher Wisdom\n(Taparloka)",
        "Layer 7: 4th Dimension\n(Higher Consciousness)",
        "Layer 8: 5th Dimension\n(Anupadaka Plane)",
        "Layer 9: 6th Dimension\n(Adi Plane)",
        "Layer 10: Beyond Dimensions\n(Beyond all known planes)"
    ]

    for position, label in zip(label_positions, labels):
        ax.text(position[0], position[1], label, ha='center', va='center', fontsize=10, color='black', wrap=True)

fig, ax = plt.subplots(figsize=(16, 16))
ax.set_aspect('equal')
draw_seed_of_life(ax, radius=2, num_layers=11)
draw_labels(ax, radius=2)
plt.xlim(-10, 10)
plt.ylim(-10, 10)
plt.axis('off')
plt.title("Expanded Seed of Life with 11 Dimensions of Consciousness", fontsize=16)
plt.show()

Save the script as: 11layerviz.py

Run the script: python 11layerviz.py

Updated Neural Network Design

Let’s expand the neural network to include all 11 layers.

Python Script to Create the Neural Network with 11 Layers

import tensorflow as tf
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.preprocessing import image_dataset_from_directory
import matplotlib.pyplot as plt
import numpy as np

# Define a function to create a scalable model
def create_model(input_dim, output_dim, layer_configs):
    model = Sequential()
    model.add(Input(shape=(input_dim,)))
    
    for config in layer_configs:
        model.add(Dense(units=config['units'], activation='relu', name=config['name']))
    
    model.add(Dense(units=output_dim, activation='softmax', name='Output_Layer'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

# Example layer configurations
layer_configs = [
    {'name': 'Eternal_Now', 'units': 64},                    # Layer 0: Information
    {'name': 'Awareness_in_the_Now', 'units': 128},          # Layer 1: Information
    {'name': 'Consciousness_of_Awareness', 'units': 256},    # Layer 2: Information
    {'name': 'Intuition', 'units': 128},                     # Layer 3: Knowledge
    {'name': 'Divine_Intelligence', 'units': 64},            # Layer 4: Knowledge
    {'name': 'Inner_Knowing', 'units': 32},                  # Layer 5: Knowledge
    {'name': 'Higher_Wisdom', 'units': 16},                  # Layer 6: Realization
    {'name': '4th_Dimension', 'units': 8},                   # Layer 7: Realization
    {'name': '5th_Dimension', 'units': 4},                   # Layer 8: Realization
    {'name': '6th_Dimension', 'units': 2},                   # Layer 9: Realization
    {'name': 'Beyond_Dimensions', 'units': 1}                # Layer 10: Realization
]

input_dim = 100  # Example input dimension
output_dim = 10  # Example output dimension
model = create_model(input_dim, output_dim, layer_configs)
model.summary()

# Load initial dataset
train_data = image_dataset_from_directory('path/to/dataset', batch_size=32, image_size=(256, 256))

# Function to integrate new data
def integrate_new_data(new_data_path, batch_size=32, image_size=(256, 256)):
    new_data = image_dataset_from_directory(new_data_path, batch_size=batch_size, image_size=image_size)
    return new_data

# Example usage
new_train_data = integrate_new_data('path/to/new_dataset')

# Incrementally train with new data
def incremental_training(model, new_data, epochs=10):
    model.fit(new_data, epochs=epochs)
    model.save('path/to/updated_model.h5')

# Example usage
incremental_training(model, new_train_data)

# Visualization functions
def draw_circle(ax, center, radius):
    circle = plt.Circle(center, radius, edgecolor='b', facecolor='none')
    ax.add_artist(circle)

def draw_seed_of_life(ax, radius, num_layers):
    centers = [(0, 0)]
    angle_step = 2 * np.pi / num_layers
    
    for i in range(1, num_layers + 1):
        angle = i * angle_step
        x = radius * np.cos(angle)
        y = radius * np.sin(angle)
        centers.append((x, y))
    
    for center in centers:
        draw_circle(ax, center, radius)

fig, ax = plt.subplots(figsize=(16, 16))
ax.set_aspect('equal')
draw_seed_of_life(ax, radius=2, num_layers=11)
plt.xlim(-10, 10)
plt.ylim(-10, 10)
plt.axis('off')
plt.title("Expanded Seed of Life with 11 Dimensions of Consciousness", fontsize=16)
plt.show()

Running the Script: python deep_layer_knowing_network.py

Sources: GPT 4o

Stay in the NOW with Inner I Network;

---