code: def kaiming_normal_gain(shape: Tuple[int, ...]) -> float:
      """Returns Kaiming He gain from
      `Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
      <https://arxiv.org/abs/1502.01852>`_.
  
      Args:
          shape: shape of the output tensor.
  
      Returns:
          Scalar, the standard deviation gain.
      """
      fan_in = np.prod(shape[:-1])
      return np.sqrt(1 / fan_in)
  
file_path: objax/objax/nn/init.py
imports: from typing import Tuple
  import numpy
  import scipy.stats
  from jax import numpy
  from objax import random
  from objax.typing import JaxArray
label_desc: Use image node_id 4 for calling a global function with example usage: kaiming_normal_gain(shape) and returns: np