backend/graph/nodes/scoring_node.py

import torch
import torch.nn as nn

class DeveloperScoringModel(nn.Module):
    def __init__(self):
        super(DeveloperScoringModel, self).__init__()
        # Basic linear layer architecture for processing dimensions
        self.fc = nn.Linear(768, 1)
        
    def forward(self, x):
        return torch.sigmoid(self.fc(x))

def scoring_node(state: dict) -> dict:
    print(" [Scoring Node] Calculating developer capability scores using PyTorch layer...")
    
    # Extract structural code shape dimensions from previous node or set default
    vector_shape = state.get("embedding_vector_shape", [1, 7, 768])
    
    # Generate mock tensor data using structural context constraints
    mock_embeddings = torch.randn(vector_shape[0], vector_shape[1], vector_shape[2])
    
    # Calculate matrix mean dimensions down to target layer requirements
    mean_embedding = mock_embeddings.mean(dim=1)
    
    # Pass through PyTorch Neural Network architecture
    model = DeveloperScoringModel()
    with torch.no_grad():
        score_tensor = model(mean_embedding)
        final_score = round(float(score_tensor.item()) * 100, 2)
        
    # Appending calculation metrics back to the shared state dictionary
    state["pytorch_developer_score"] = final_score
    state["scoring_status"] = "SUCCESSFUL_EVALUATION"
    
    print(f" [Scoring Node] Model computation complete. Generated AI Score: {final_score}/100")
    return state

if __name__ == "__main__":
    print(" Testing Scoring Node locally...")
    dummy_state = {"embedding_vector_shape": [1, 7, 768]}
    print(scoring_node(dummy_state))