Implement Multi-Head Self-Attention

Implement a **multi-head self-attention** module in **PyTorch** without using `torch.nn.MultiheadAttention`. Requirements: - Input tensor shape: `(batch_size, seq_len, d_model)` - Number of heads: `num_heads`, where `d_model % num_heads == 0` - Use learned linear projections to produce **Q**, **K**, and **V** from the same input - Split the projected tensors into multiple heads - Compute scaled dot-product attention for each head: `Attention(Q, K, V) = softmax(Q K^T / sqrt(head_dim)) V` - Support an optional attention mask so masked positions do not contribute to attention scores - Concatenate all heads and apply a final output projection - Return a tensor of shape `(batch_size, seq_len, d_model)` You may assume standard PyTorch layers such as `nn.Linear`, `torch.matmul`, `softmax`, `view`, and `transpose` are available. Explain any important tensor shape transformations and common implementation pitfalls.