chunk_content: method; P-tuning also adds a trainable embedding tensor that can be optimized to find better prompts, and it uses a prompt encoder (a bidirectional long-short term memory network or LSTM) to optimize the prompt parameters. Unlike prefix tuning though:
  the prompt tokens can be inserted anywhere in the input sequence, and it isn’t restricted to only the beginning
  the prompt tokens are only added to the input instead of adding them to every layer 