class_docstr: Test loss function for testing BoltOn model.
class_docstr_tok: ["Test","loss","function","for","testing","BoltOn","model","."]
docstr: Smoothness, beta.
  
  Args:
    class_weight: the class weights as scalar or 1d tensor, where its
      dimensionality is equal to the number of outputs.
  
  Returns:
    Beta
docstr_tok: ["Smoothness",",","beta",".","Args",":","class_weight",":","the","class","weights","as","scalar","or","1d","tensor",",","where","its","dimensionality","is","equal","to","the","number","of","outputs",".","Returns",":","Beta"]
code: def beta(self, class_weight):  # pylint: disable=unused-argument
      """Smoothness, beta.
  
      Args:
        class_weight: the class weights as scalar or 1d tensor, where its
          dimensionality is equal to the number of outputs.
  
      Returns:
        Beta
      """
      return _ops.convert_to_tensor_v2(1, dtype=tf.float32)
  
imports: import unittest
  from absl.testing import parameterized
  import tensorflow.compat.v1
  from tensorflow.compat.v1.python import ops
  from tensorflow.compat.v1.python.framework import test_util
  from tensorflow.compat.v1.python.keras import keras_parameterized
  from tensorflow.compat.v1.python.keras import losses
  from tensorflow.compat.v1.python.keras.initializers import constant
  from tensorflow.compat.v1.python.keras.models import Model
  from tensorflow.compat.v1.python.keras.optimizer_v2.optimizer_v2 import OptimizerV2
  from tensorflow.compat.v1.python.keras.regularizers import L1L2
  from tensorflow.compat.v1.python.platform import test
  from tensorflow_privacy.privacy.bolt_on import optimizers
  from tensorflow_privacy.privacy.bolt_on.losses import StrongConvexMixin
label_desc: Use image node_id 4 for calling the TestLoss obj's underlying member method code with example usage: obj.beta(class_weight) and returns: _ops