class_docstr: He uniform variance scaling initializer.
  
  Draws samples from a uniform distribution within `[-limit, limit]`, where
  `limit = sqrt(6 / fan_in)` (`fan_in` is the number of input units in the
  weight tensor).
  
  Examples:
  
  >>> # Standalone usage:
  >>> initializer = HeUniform()
  >>> values = initializer(shape=(2, 2))
  
  >>> # Usage in a Keras layer:
  >>> initializer = HeUniform()
  >>> layer = Dense(3, kernel_initializer=initializer)
  
  Args:
      seed: A Python integer or instance of
          `keras.backend.SeedGenerator`.
          Used to make the behavior of the initializer
          deterministic. Note that an initializer seeded with an integer
          or `None` (unseeded) will produce the same random values
          across multiple calls. To get different random values
          across multiple calls, use as seed an instance
          of `keras.backend.SeedGenerator`.
  
  Reference:
  
  - [He et al., 2015](https://arxiv.org/abs/1502.01852)
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code: def __init__(self, seed=None):
      super().__init__(
          scale=2.0, mode="fan_in", distribution="uniform", seed=seed
      )
  
imports: import math
  from keras import ops
  from keras.api_export import keras_export
  from keras.backend import random
  from keras.initializers.initializer import Initializer
  from keras.saving import serialization_lib
label_desc: Use image node_id 1 to create a new HeUniform object from inherited base classes: VarianceScaling with example: obj = HeUniform(seed)