class_docstr: Class providing a method for fast attention via low rank decomposition. Class is responsible for providing a method
  <dot_product_attention> for fast dot-product attention with the use of low rank decomposition (e.g. with random
  feature maps).
class_docstr_tok: ["Class","providing","a","method","for","fast","attention","via","low","rank","decomposition",".","Class","is","responsible","for","providing","a","method","<","dot_product_attention",">","for","fast","dot-product","attention","with","the","use","of","low","rank","decomposition","(","e.g",".","with","random","feature","maps",")","."]
code: def __init__(
      self,
      matrix_creator,
      kernel_feature_creator,
      renormalize_attention,
      numerical_stabilizer,
      redraw_features,
      unidirectional,
      lax_scan_unroll=1,
  ):  # For optimal GPU performance, set to 16.
      rng = random.PRNGKey(0)
      self.matrix_creator = matrix_creator
      self.projection_matrix = self.draw_weights(rng)
      self.kernel_feature_creator = kernel_feature_creator
      self.renormalize_attention = renormalize_attention
      self.numerical_stabilizer = numerical_stabilizer
      self.redraw_features = redraw_features
      self.unidirectional = unidirectional
      self.lax_scan_unroll = lax_scan_unroll
  
imports: import abc
  import functools
  from collections.abc import Iterable
  import numpy
  from absl import logging
  import jax
  import jax.numpy
  from jax import lax, random
label_desc: Use image node_id 1 to create a new FastAttentionviaLowRankDecomposition object from inherited base classes: FastAttention with example: obj = FastAttentionviaLowRankDecomposition(matrix_creator, kernel_feature_creator, renormalize_attention, numerical_stabilizer, redraw_features, unidirectional, lax_scan_unroll)