Design a Scalable Visual Representation Framework Using Patchwise Feature Aggregation (ViT)

Question

## Problem Description

Modern perception systems often require **high-dimensional feature extraction** pipelines that can scale efficiently with input resolution and model depth.  
Your task is to implement a **modular visual representation framework** capable of handling inputs of varying granularity while maintaining global consistency across patch-level features.

The design should incorporate:
- **Local-to-global information fusion**
- **Learned contextual interaction between tokens**
- **Efficient hierarchical computation using only primitive PyTorch operations**

The underlying framework must follow a **token-based architecture**, where each visual unit contributes to a shared representation space.  
You must write all components **from scratch**, avoiding prebuilt transformer utilities or external attention modules.

At the end, implement this system following the **Vision Transformer (ViT)** architecture, including patch embedding, positional encoding, transformer encoder blocks, and classification head — all implemented manually using basic PyTorch functions.

---

## Requirements

Implement the following:

1. **Patch-Level Encoder**
   - Divide the image into smaller seg

Accepted Answer

A detailed reference solution is available to Crack ML Interview members.