Your task is to implement a single transformer block, a key component in large language models (LLMs) like GPT. This block includes:

Multi-Head Self-Attention:

Calculate query, key, and value matrices. Compute scaled dot-product attention. Concatenate outputs from multiple attention heads.

Feed-Forward Network:

Apply a linear transformation, followed by a non-linear activation (e.g., ReLU). Apply another linear transformation to project back to the original dimension.

Layer Normalization:

Normalize the outputs of the self-attention and feed-forward layers.

Residual Connections:

Add the input of each sub-layer to its output for better gradient flow.

Requirements:

Language: Python Libraries: PyTorch or TensorFlow Input/Output: Process and transform variable-length input sequences.

Implement this transformer block to understand its role in building LLMs.