class_docstr: Handles the packing/transformation of estimated parameters.
class_docstr_tok: ["Handles","the","packing\/transformation","of","estimated","parameters","."]
docstr: Unpack and return the beta parameter.
docstr_tok: ["Unpack","and","return","the","beta","parameter","."]
code: def get_beta(
      self, packed_optimizer_args: types.RealTensor
  ) -> types.RealTensor:
      """Unpack and return the beta parameter."""
      if self.calibrate_beta:
          beta = packed_optimizer_args[..., -1]
          return _to_constrained(
              beta, self.beta_lower_bound, self.beta_upper_bound
          )
      else:
          return self.beta
  
imports: from typing import Callable, Tuple
  import tensorflow.compat.v2
  from tf_quant_finance import black_scholes
  from tf_quant_finance import types
  from tf_quant_finance import utils
  from tf_quant_finance.black_scholes.implied_vol_utils import UnderlyingDistribution
  from tf_quant_finance.math import make_val_and_grad_fn
  from tf_quant_finance.math import optimizer
  from tf_quant_finance.models.sabr import approximations
  from tf_quant_finance.models.sabr.approximations.implied_volatility import SabrApproximationType
  from tf_quant_finance.models.sabr.approximations.implied_volatility import SabrImpliedVolatilityType
label_desc: Use image node_id 4 for calling the _OptimizerArgHandler obj's underlying member method code with example usage: obj.get_beta(packed_optimizer_args) and returns: _to_constrained, self