class_docstr: Wraps the function `feature_steered_convolution` as a Keras layer.
class_docstr_tok: ["Wraps","the","function","`","feature_steered_convolution","`","as","a","Keras","layer","."]
docstr: Initializes FeatureSteeredConvolutionKerasLayer.
  
  Args:
    translation_invariant: A `bool`. If `True` the assignment of features to
      weight matrices will be invariant to translation.
    num_weight_matrices: An `int` specifying the number of weight matrices
      used in the convolution.
    num_output_channels: An optional `int` specifying the number of channels
      in the output. If `None` then `num_output_channels` will be the same as
      the input dimensionality.
    initializer: An initializer for the trainable variables. If `None`,
      defaults to `tf.keras.initializers.TruncatedNormal(stddev=0.1)`.
    name: A name for this layer.
    **kwargs: Additional keyword arguments passed to the base layer.
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code: def __init__(
      self,
      translation_invariant=True,
      num_weight_matrices=8,
      num_output_channels=None,
      initializer=None,
      name=None,
      **kwargs,
  ):
      """Initializes FeatureSteeredConvolutionKerasLayer.
  
      Args:
        translation_invariant: A `bool`. If `True` the assignment of features to
          weight matrices will be invariant to translation.
        num_weight_matrices: An `int` specifying the number of weight matrices
          used in the convolution.
        num_output_channels: An optional `int` specifying the number of channels
          in the output. If `None` then `num_output_channels` will be the same as
          the input dimensionality.
        initializer: An initializer for the trainable variables. If `None`,
          defaults to `tf.keras.initializers.TruncatedNormal(stddev=0.1)`.
        name: A name for this layer.
        **kwargs: Additional keyword arguments passed to the base layer.
      """
      super(FeatureSteeredConvolutionKerasLayer, self).__init__(
          name=name, **kwargs
      )
      self._num_weight_matrices = num_weight_matrices
      self._num_output_channels = num_output_channels
      self._translation_invariant = translation_invariant
      if initializer is None:
          self._initializer = tf.keras.initializers.TruncatedNormal(
              stddev=0.1
          )
      else:
          self._initializer = initializer
  
file_path: graphics/tensorflow_graphics/nn/layer/graph_convolution.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  import tensorflow_graphics.geometry.convolution.graph_convolution
  from tensorflow_graphics.util import export_api
label_desc: Use image node_id 1 to create a new FeatureSteeredConvolutionKerasLayer object from inherited base classes: tf.keras.layers with example: obj = FeatureSteeredConvolutionKerasLayer(translation_invariant, num_weight_matrices, num_output_channels, initializer, name)