code: def get_q_kv_mask(
      x: jnp.ndarray,
      input_mask: Optional[jnp.ndarray],
      layer: int,
      tape_added: int,
      ac_config: ml_collections.ConfigDict,
      bank: Optional[jnp.ndarray],
      train: bool,
  ) -> Tuple[
      jnp.ndarray,
      Optional[jnp.ndarray],
      Optional[jnp.ndarray],
      Optional[jnp.ndarray],
      Optional[jnp.ndarray],
      int,
  ]:
      """Generates query, key/valye, input mask and logging input mask based on ac_config."""
  
      # Prepare x and taped_x, if necessary:
      if layer in ac_config.add_tape_token_to_layers:
          # For layers where we add tape token:
          taped_x, (loss_atr, n_updates) = AddTapeToken(
              ac_config=ac_config
          )(x, bank=bank, train=train)
          tape_added += taped_x.shape[1] - x.shape[1]
          # Correct n_updates when the num_token_per_step > 1.
          if ac_config.dynamic_tape_length:
              n_updates = (
                  n_updates
                  * ac_config.dynamic_tape_length.num_token_per_step
              )
  
          input_mask, logging_input_mask, taped_x = update_input_mask(
              input_mask=input_mask,
              taped_x=taped_x,
              ada_tt_len=n_updates,
              ac_config=ac_config,
          )
  
          # Prepare query and key/value (memory):
          x_q, x_kv = taped_x, None
  
          return (
              x_q,
              x_kv,
              input_mask,
              logging_input_mask,
              loss_atr,
              tape_added,
          )
  
      elif tape_added:
          # For layers after adding tape tokens, where we don't add tape token.
          #   taped_x = x
          #   x = x[:, :-tape_added, :]
          x_q, x_kv = x, None
          return x_q, x_kv, input_mask, input_mask, None, tape_added
      else:
          # For layers before adding tape tokens, just run self attention:
          return x, None, input_mask, input_mask, None, 0
  
imports: from typing import Any, Callable, Optional, Sequence, Tuple
  from absl import logging
  import flax.linen
  import jax
  import jax.numpy
  import ml_collections
  from scenic.model_lib.layers import nn_layers
label_desc: Use image node_id 3 for calling a global function with example usage: get_q_kv_mask(x, input_mask, layer, tape_added, ac_config, bank, train) and returns: x_q,  x_kv,  input_mask,  logging_input_mask,  loss_atr,  tape_added, x_q,  x_kv,  input_mask,  input_mask,  None,  tape_added, x,  None,  input_mask,  input_mask,  None,  int