class_docstr: Strong Convex Mixin base class.
  
  Strong Convex Mixin base class for any loss function that will be used with
  BoltOn model. Subclasses must be strongly convex and implement the
  associated constants. They must also conform to the requirements of tf losses
  (see super class).
  
  For more details on the strong convexity requirements, see:
  Bolt-on Differential Privacy for Scalable Stochastic Gradient
  Descent-based Analytics by Xi Wu et. al.
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docstr: Returns strongly convex parameter, gamma.
docstr_tok: ["Returns","strongly","convex","parameter",",","gamma","."]
code: def gamma(self):
      """Returns strongly convex parameter, gamma."""
      raise NotImplementedError(
          "Gamma not implemented for StrongConvex Loss"
          "function: %s" % str(self.__class__.__name__)
      )
  
imports: import tensorflow.compat.v1
  from tensorflow.compat.v1.python.framework import ops
  from tensorflow.compat.v1.python.keras import losses
  from tensorflow.compat.v1.python.keras.regularizers import L1L2
  from tensorflow.compat.v1.python.keras.utils import losses_utils
  from tensorflow.compat.v1.python.platform import tf_logging
label_desc: Use image node_id 2 for calling the StrongConvexMixin obj's underlying member method code with example usage: obj.gamma() without return types