class_docstr: Parallel module takes a function or a module and compiles it for running on multiple devices in parallel.
class_docstr_tok: ["Parallel","module","takes","a","function","or","a","module","and","compiles","it","for","running","on","multiple","devices","in","parallel","."]
code: def __repr__(self):
      args = dict(
          f=self.__wrapped__,
          reduce=repr_function(self.reduce),
          axis_name=repr(self.axis_name),
          static_argnums=tuple(sorted(self.static_argnums)) or None,
      )
      args = ", ".join(f"{k}={v}" for k, v in args.items())
      return f"{class_name(self)}({args})"
  
imports: from collections import namedtuple
  from typing import Optional, List, Union, Callable, Tuple
  import jax
  import jax.numpy
  import numpy
  from objax.typing import JaxArray
  from objax.util import class_name, override_args_kwargs, positional_args_names, repr_function
  from objax.variable import BaseVar, RandomState, VarCollection
label_desc: Use image node_id 4 for calling the Parallel obj's underlying member method code with example usage: obj.__repr__() and returns: class_name, str, args, str