class_docstr: Helper to compute basic NumPy-like statistics.
class_docstr_tok: ["Helper","to","compute","basic","NumPy-like","statistics","."]
code: def __call__(self, x, axis=0, dtype=None, xp=None):
      if axis is None:
          axis = tuple(range(x.ndim))
      elif not isinstance(axis, (tuple, list)):
          axis = (axis,)
  
      return self.collect(x, axis)
  
imports: from __future__ import division
  import os
  import warnings
  import numpy
  import six
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer.training import extension
  from chainer.training import trigger
  from chainer.utils import argument
label_desc: Use image node_id 2 for calling the Statistician obj's underlying member method code with example usage: obj.__call__(x, axis, dtype, xp) and returns: self