class_docstr: Handles the packing/transformation of estimated parameters.
class_docstr_tok: ["Handles","the","packing\/transformation","of","estimated","parameters","."]
docstr: Unpack and return the alpha parameter.
docstr_tok: ["Unpack","and","return","the","alpha","parameter","."]
code: def get_alpha(
      self, packed_optimizer_args: types.RealTensor
  ) -> types.RealTensor:
      """Unpack and return the alpha parameter."""
      alpha = packed_optimizer_args[..., 0]
      return _to_constrained(
          alpha, self.alpha_lower_bound, self.alpha_upper_bound
      )
  
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 1 for calling the _OptimizerArgHandler obj's underlying member method code with example usage: obj.get_alpha(packed_optimizer_args) and returns: _to_constrained