code: def he_uniform(seed=None):
      # pylint: disable=line-too-long
      """He uniform variance scaling initializer.
  
      It draws samples from a uniform distribution within [-limit, limit]
      where `limit` is `sqrt(6 / fan_in)`
      where `fan_in` is the number of input units in the weight tensor.
  
      Args:
        seed: PRNG seed; see `tfp.random.sanitize_seed` for details.
          Default value: `None`.
  
      Returns:
        init_fn: A python `callable` which takes a shape `Tensor`, dtype and an
          optional scalar `int` number of batch dims and returns a randomly
          initialized `Tensor` with the specified shape and dtype.
  
      References:
        [He et al., 2015]
        (https://www.cv-foundation.org/openaccess/content_iccv_2015/html/He_Delving_Deep_into_ICCV_2015_paper.html)
        ([pdf](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf))
      """
      # pylint: enable=line-too-long
      return lambda shape, dtype, batch_ndims=0: _initialize(  # pylint: disable=g-long-lambda
          shape,
          dtype,
          batch_ndims,
          scale=2.0,
          mode="fan_in",
          distribution="uniform",
          seed=seed,
      )
  
file_path: probability/tensorflow_probability/python/experimental/nn/initializers/initializers.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
label_desc: Use image node_id 4 for calling a global function with example usage: he_uniform(seed) and returns: function