<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def while_loop(cond_fn, body_fn, inputs, num_loop_vars=None, has_accumulators=False, **kwargs):
 """While Loop. See comments above for WhileLoopOperation num_loop_vars is a hack for the multi-gpu setup. In this case, loops are generally slow, as all loop variables are placed on device. By setting num_loop_vars=k, then all of the loop variables except for the first k are handled as mtf Variables instead of loop variables, using explicit updates and control dependencies. In this case, we only return the first num_loop_vars outputs. Do not use this option on TPU, since it is unnecessary and also produces incorrect results, since xla does not respect control dependencies. Args: cond_fn: a function from n Tensors to scalar boolean Tensor body_fn: a function from n Tensors to list of n Tensors inputs: a list of n Tensors num_loop_vars: an optional integer. has_accumulators: a boolean **kwargs: additional kwargs passed to tf.while_loop Returns: a list of n Tensors. """

  if num_loop_vars is None:
    return WhileLoopOperation(cond_fn, body_fn, inputs, tf_kwargs=kwargs,
                              has_accumulators=has_accumulators).outputs
  # Turn all loop vars except for the first ones into non-loop vars.
  # see comments in docstring.
  assert num_loop_vars > 0
  extra_inputs = inputs[num_loop_vars:]
  my_vars = []
  for i, x in enumerate(extra_inputs):
    my_vars.append(get_variable(
        x.mesh, "loop_var_%d" % i,
        x.shape, initializer=tf.zeros_initializer(),
        dtype=x.dtype,
        collections=[tf.GraphKeys.LOCAL_VARIABLES]))
  my_vars = tuple(my_vars)
  first_input = depend(
      inputs[0], [assign(var, x) for var, x in zip(my_vars, extra_inputs)])
  inputs = [first_input] + inputs[1:num_loop_vars]
  def my_cond_fn(*inputs):
    return cond_fn(*(inputs + my_vars))
  def my_body_fn(*inputs):
    outputs = tuple(body_fn(*(inputs + my_vars)))
    extra_outputs = outputs[num_loop_vars:]
    first_output = depend(
        outputs[0], [assign(var, x) for var, x in zip(my_vars, extra_outputs)])
    outputs = (first_output,) + outputs[1:num_loop_vars]
    return outputs
  return WhileLoopOperation(
      my_cond_fn, my_body_fn, inputs, tf_kwargs=kwargs,
      has_accumulators=has_accumulators).outputs