class_docstr: Generates the Parallax Strategy from https://arxiv.org/pdf/1808.02621.pdf.
  
  The Parallax strategy mixes Parameter Server and AllReduce. The rationale is that
  a PS architecture is more suitable for sparse gradient updates, while AllReduce
  has reportedly better performance on dense gradient updates.
code: def __init__(
      self,
      chunk_size=128,
      local_proxy_variable=False,
      sync=True,
      staleness=0,
  ):
      PSLoadBalancing.__init__(
          self, local_proxy_variable, sync, staleness
      )
      AllReduce.__init__(self, chunk_size)
  
file_path: autodist/autodist/strategy/parallax_strategy.py
imports: from tensorflow.python.framework import ops
  from autodist.strategy.base import Strategy
  from autodist.strategy.ps_lb_strategy import PSLoadBalancing
  from autodist.strategy.all_reduce_strategy import AllReduce
  from autodist.kernel.common.utils import get_op_name
label_desc: Use image node_id 1 to create a new Parallax object from inherited base classes: PSLoadBalancing, AllReduce with example: obj = Parallax(chunk_size, local_proxy_variable, sync, staleness)