class_docstr: The Vanilla PointNet feature encoder.
  
  Consists of five conv2 layers with (64,64,64,128,1024) output channels.
  
  Note:
    PointNetConv2Layer are used instead of tf.keras.layers.Conv2D.
  
  https://github.com/charlesq34/pointnet/blob/master/models/pointnet_cls_basic.py
class_docstr_tok: ["The","Vanilla","PointNet","feature","encoder",".","Consists","of","five","conv2","layers","with","(","64,64,64,128,1024",")","output","channels",".","Note",":","PointNetConv2Layer","are","used","instead","of","tf.keras.layers.Conv2D",".","https",":","\/\/github.com\/charlesq34\/pointnet\/blob\/master\/models\/pointnet_cls_basic.py"]
docstr: Constructs a VanillaEncoder keras layer.
  
  Args:
    momentum: the momentum used for the batch normalization layer.
docstr_tok: ["Constructs","a","VanillaEncoder","keras","layer",".","Args",":","momentum",":","the","momentum","used","for","the","batch","normalization","layer","."]
code: def __init__(self, momentum: float = 0.5):
      """Constructs a VanillaEncoder keras layer.
  
      Args:
        momentum: the momentum used for the batch normalization layer.
      """
      super(VanillaEncoder, self).__init__()
      self.conv1 = PointNetConv2Layer(64, momentum)
      self.conv2 = PointNetConv2Layer(64, momentum)
      self.conv3 = PointNetConv2Layer(64, momentum)
      self.conv4 = PointNetConv2Layer(128, momentum)
      self.conv5 = PointNetConv2Layer(1024, momentum)
  
imports: from typing import Optional
  import tensorflow
  from tensorflow_graphics.util import export_api
label_desc: Use image node_id 1 to create a new VanillaEncoder object from inherited base classes: tf.keras.layers with example: obj = VanillaEncoder(momentum)