class_docstr: Nonnegative parameterization as needed for GDN parameters.
  
  The variable is subjected to an invertible transformation that slows down the
  learning rate for small values.
  
  Attributes:
    minimum: Float. The `minimum` parameter provided on initialization.
    offset: Float. The `offset` parameter provided on initialization.
    variable: `tf.Variable`. The reparameterized variable.
code: def get_config(self) -> Dict[str, Any]:
      config = super().get_config()
      config.update(
          initial_value=None,
          minimum=self.minimum,
          offset=self.offset,
          shape=tuple(map(int, self.variable.shape)),
          dtype=self.variable.dtype.name,
      )
      return config
  
imports: import abc
  from typing import Any, Dict
  import tensorflow
  from tensorflow_compression.python.ops import math_ops
label_desc: Use image node_id 5 for calling the GDNParameter obj's underlying member method code with example usage: obj.get_config() and returns: config