class_docstr: The PointNet 'Vanilla' classifier (i.e. without spatial transformer).
class_docstr_tok: ["The","PointNet","'Vanilla","'","classifier","(","i.e",".","without","spatial","transformer",")","."]
docstr: Computes the classifiation logits of a point set.
  
  Args:
    points: a tensor of points with shape `[B, D]`
    training: for batch normalization and dropout training.
  
  Returns:
    Tensor with shape `[B,num_classes]`
docstr_tok: ["Computes","the","classifiation","logits","of","a","point","set",".","Args",":","points",":","a","tensor","of","points","with","shape","`","[","B",",","D","]","`","training",":","for","batch","normalization","and","dropout","training",".","Returns",":","Tensor","with","shape","`","[","B",",","num_classes","]","`"]
code: def call(
      self, points: tf.Tensor, training: Optional[bool] = None
  ) -> tf.Tensor:  # pylint: disable=arguments-differ
      """Computes the classifiation logits of a point set.
  
      Args:
        points: a tensor of points with shape `[B, D]`
        training: for batch normalization and dropout training.
  
      Returns:
        Tensor with shape `[B,num_classes]`
      """
      features = self.encoder(points, training)  # (B,1024)
      logits = self.classifier(features, training)  # (B,num_classes)
      return logits
  
imports: from typing import Optional
  import tensorflow
  from tensorflow_graphics.util import export_api
label_desc: Use image node_id 2 for calling the PointNetVanillaClassifier obj's underlying member method code with example usage: obj.call(points, training) and returns: logits