class_docstr: The PointNet classification head.
  
  The head consists of 2x PointNetDenseLayer layers (512 and 256 channels)
  followed by a dropout layer (drop rate=30%) a dense linear layer producing the
  logits of the num_classes classes.
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docstr: Computes the classifiation logits given features (note: without softmax).
  
  Args:
    inputs: tensor of points with shape `[B,D]`.
    training: flag for batch normalization and dropout training.
  
  Returns:
    Tensor with shape `[B,num_classes]`
docstr_tok: ["Computes","the","classifiation","logits","given","features","(","note",":","without","softmax",")",".","Args",":","inputs",":","tensor","of","points","with","shape","`","[","B",",","D","]","`",".","training",":","flag","for","batch","normalization","and","dropout","training",".","Returns",":","Tensor","with","shape","`","[","B",",","num_classes","]","`"]
code: def call(
      self, inputs: tf.Tensor, training: Optional[bool] = None
  ) -> tf.Tensor:  # pylint: disable=arguments-differ
      """Computes the classifiation logits given features (note: without softmax).
  
      Args:
        inputs: tensor of points with shape `[B,D]`.
        training: flag for batch normalization and dropout training.
  
      Returns:
        Tensor with shape `[B,num_classes]`
      """
      x = self.dense1(inputs, training)  # [B,512]
      x = self.dense2(x, training)  # [B,256]
      x = self.dropout(x, training)  # [B,256]
      return self.dense3(x)
  
imports: from typing import Optional
  import tensorflow
  from tensorflow_graphics.util import export_api
label_desc: Use image node_id 2 for calling the ClassificationHead obj's underlying member method code with example usage: obj.call(inputs, training) and returns: self