class_docstr: Initializer that generates an orthogonal matrix.
  
  If the shape of the tensor to initialize is two-dimensional, it is
  initialized with an orthogonal matrix obtained from the QR decomposition of
  a matrix of random numbers drawn from a normal distribution. If the matrix
  has fewer rows than columns then the output will have orthogonal rows.
  Otherwise, the output will have orthogonal columns.
  
  If the shape of the tensor to initialize is more than two-dimensional,
  a matrix of shape `(shape[0] * ... * shape[n - 2], shape[n - 1])`
  is initialized, where `n` is the length of the shape vector.
  The matrix is subsequently reshaped to give a tensor of the desired shape.
  
  Examples:
  
  >>> # Standalone usage:
  >>> initializer = keras.initializers.Orthogonal()
  >>> values = initializer(shape=(2, 2))
  
  >>> # Usage in a Keras layer:
  >>> initializer = keras.initializers.Orthogonal()
  >>> layer = keras.layers.Dense(3, kernel_initializer=initializer)
  
  Args:
      gain: Multiplicative factor to apply to the orthogonal matrix.
      seed: A Python integer. Used to make the behavior of the initializer
          deterministic.
  
  Reference:
  
  - [Saxe et al., 2014](https://openreview.net/forum?id=_wzZwKpTDF_9C)
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code: def get_config(self):
      seed_config = serialization_lib.serialize_keras_object(
          self._init_seed
      )
      return {"gain": self.gain, "seed": seed_config}
  
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 3 for calling the OrthogonalInitializer obj's underlying member method code with example usage: obj.get_config() and returns: dict