code: def he_uniform(name, shape, scale=1, dtype=tf.sg_floatx, summary=True, regularizer=None, trainable=True):
      r
      fin, _ = _get_fans(shape)
      s = np.sqrt(1. * scale / fin)
      return uniform(name, shape, s, dtype, summary, regularizer, trainable)
docs: r"""See [He et al. 2015](http://arxiv.org/pdf/1502.01852v1.pdf)
  
      Args:
        name: The name of new variable
        shape: A tuple/list of integers.
        scale: A Python scalar. Scale to initialize. Default is 1.
        dtype: The data type. Default is float32.
        summary: If True, add this constant to tensor board summary.
        regularizer:  A (Tensor -> Tensor or None) function; the result of applying it on a newly created variable
          will be added to the collection tf.GraphKeys.REGULARIZATION_LOSSES and can be used for regularization
        trainable: If True, add this constant to trainable collection. Default is True.
  
      Returns:
        A `Variable`.