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 functools
  from collections.abc import Iterable
  import jax
  import jax.numpy
  import numpy
  from absl import logging
  from jax import lax, random
label_desc: Use image node_id 3 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