Give me a description, to serve as a comment into a Python class, of what an TransformerEncoder block does.
Sure, here's an example description of a TransformerEncoder block in Python:

class TransformerEncoder:
    
    A TransformerEncoder block applies multi-head self-attention and feedforward neural network to input sequence to generate 
    encoded representation of the input. In natural language processing, it is often used to encode a sequence of tokens such 
    as words or sentences, to generate embeddings for tasks such as language modeling, machine translation, or sentiment analysis. 
    The multi-head self-attention mechanism enables the encoder to attend to different parts of the input sequence simultaneously, 
    and the feedforward neural network provides non-linear transformations for the input embeddings. The output of the 
    TransformerEncoder block is a tensor of the same shape as the input tensor, but with each element being a learned encoding 
    of the corresponding element in the input. 
    

This comment describes a TransformerEncoder block as a component of a Transformer neural network that applies multi-head self-attention and feedforward neural network to an input sequence to generate an encoded representation of the input. It highlights the potential applications of a TransformerEncoder block in natural language processing and its ability to attend to different parts of an input sequence simultaneously. Finally, it notes that the output of the TransformerEncoder block is a tensor of the same shape as the input tensor, but with each element being a learned encoding of the corresponding element in the input.
That's a great summary! One additional detail to note might be that the self-attention mechanism in the TransformerEncoder block allows it to capture dependencies between different positions in the input sequence, making it well-suited for tasks that require modeling long-range dependencies, such as language modeling or machine translation.