code: def generalized_kernel_feature_creator(
      data,
      projection_matrix,
      batch_dims_t,
      precision,
      kernel_fn,
      kernel_epsilon,
      normalize_data,
  ):
      """Constructs kernel features for fast generalized attention.
  
      Args:
        data: input for which features are computes
        projection_matrix: matrix used to compute features
        batch_dims_t: tuple of batch dimensions
        precision: precision parameter
        kernel_fn: kernel function used
        kernel_epsilon: additive positive term added to every feature for numerical
          stability
        normalize_data: predicate indicating whether data should be normalized.
  
      Returns:
        Random features for fast generalized attention.
      """
      if normalize_data:
          data_normalizer = 1.0 / (jnp.sqrt(jnp.sqrt(data.shape[-1])))
      else:
          data_normalizer = 1.0
      if projection_matrix is None:
          return kernel_fn(data_normalizer * data) + kernel_epsilon
      else:
          data_mod_shape = (
              data.shape[0 : len(batch_dims_t)]
              + projection_matrix.shape
          )
          data_thick_random_matrix = (
              jnp.zeros(data_mod_shape) + projection_matrix
          )
          data_dash = lax.dot_general(
              data_normalizer * data,
              data_thick_random_matrix,
              (
                  (
                      (data.ndim - 1,),
                      (data_thick_random_matrix.ndim - 1,),
                  ),
                  (batch_dims_t, batch_dims_t),
              ),
              precision=precision,
          )
      data_prime = kernel_fn(data_dash) + kernel_epsilon
      return data_prime
  
imports: import abc
  import enum
  import functools
  from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Iterable
  from absl import logging
  from flax import linen
  import jax
  from jax import lax
  from jax import random
  import jax.numpy
  import numpy
  from scenic.model_lib.layers import attention_layers
label_desc: Use image node_id 8 for calling a global function with example usage: generalized_kernel_feature_creator(data, projection_matrix, batch_dims_t, precision, kernel_fn, kernel_epsilon, normalize_data) and returns: data_prime, unknown