code: def _npSuffStats(self, x, axes, shift, keep_dims):
      axis = tuple(axes)
      if shift is not None:
          m_ss = np.sum(x - shift, axis=axis, keepdims=keep_dims)
          v_ss = np.sum(
              (x - shift) * (x - shift), axis=axis, keepdims=keep_dims
          )
      else:
          m_ss = np.sum(x, axis=axis, keepdims=keep_dims)
          v_ss = np.sum(x * x, axis=axis, keepdims=keep_dims)
      count = 1.0
      for d in range(x.ndim):
          if d in set(axes):
              count *= x.shape[d]
      if not keep_dims:
          shift = np.asarray(shift)
      return count, m_ss, v_ss, shift
  
imports: import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_nn_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_impl
  import tensorflow.python.ops.nn_grad
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling the SufficientStatisticsTest obj's underlying member method code with example usage: obj._npSuffStats(x, axes, shift, keep_dims) and returns: count,  m_ss,  v_ss,  shift