explain this "import bpy
import torch
import torch.nn as nn
from torch.distributions import Categorical

# Define environment class for Blender
class BlenderEnv():
  def __init__(self):
    # Initialize Blender 
  def reset(self):
    # Reset scene
  def step(self, action):
    # Execute action and return next state, reward, done
  
# Define model architecture 
class ModelGenerator(nn.Module):
  def __init__(self, obs_size, act_size):
    super().__init__()
    # Input layer, hidden layers, output layer

  def forward(self, state):
    # Forward pass
    
  def act(self, state):
    # Get action probabilities
    probs = self.forward(state)
    dist = Categorical(probs)
    action = dist.sample()
    return action
  
  def evaluate(self, state, action):
    # Evaluate action

# Create Blender environment
env = BlenderEnv() 

# Create model  
model = ModelGenerator(obs_size, act_size)

# Define optimizer
optimizer = torch.optim.NAME_1(model.parameters())

for episode in range(num_episodes):

  state = env.reset()
  
  while True:
    
    action = model.act(state)
    
    next_state, reward, done = env.step(action)

    model.evaluate(state, action)
    
    state = next_state
    
    optimizer.zero_grad()
    loss.backward() 
    optimizer.step()
    
    if done:
      break
      
# Save trained model
torch.save(model.state_dict(), 'model.pth')"