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.
docstr: Computes and returns the non-negative value as a `tf.Tensor`.
docstr_tok: ["Computes","and","returns","the","non-negative","value","as","a","`","tf.Tensor","`","."]
code: def __call__(self, compute_dtype=None) -> tf.Tensor:
      """Computes and returns the non-negative value as a `tf.Tensor`."""
      variable = self.variable
      if compute_dtype is not None:
          variable = tf.cast(variable, compute_dtype)
      pedestal = tf.constant(self.offset**2, dtype=variable.dtype)
      bound = tf.constant(
          (self.minimum + self.offset**2) ** 0.5, dtype=variable.dtype
      )
      reparam_value = math_ops.lower_bound(variable, bound)
      return tf.math.square(reparam_value) - pedestal
  
imports: import abc
  from typing import Any, Dict
  import tensorflow
  from tensorflow_compression.python.ops import math_ops
label_desc: Use image node_id 2 for calling the GDNParameter obj's underlying member method code with example usage: obj.__call__(compute_dtype) and returns: unknown