class_docstr: Handles the packing/transformation of estimated parameters.
class_docstr_tok: ["Handles","the","packing\/transformation","of","estimated","parameters","."]
docstr: Unpack and return the rho parameter.
docstr_tok: ["Unpack","and","return","the","rho","parameter","."]
code: def get_initial_variance(
      self, packed_optimizer_args: types.RealTensor
  ) -> types.RealTensor:
      """Unpack and return the rho parameter."""
      initial_variance = packed_optimizer_args[..., 0]
      return _to_constrained(
          initial_variance,
          self.initial_variance_lower_bound,
          self.initial_variance_upper_bound,
      )
  
imports: from typing import Callable, Tuple
  import tensorflow.compat.v2
  import tf_quant_finance
  from tf_quant_finance import types
  from tf_quant_finance import utils
  from tf_quant_finance.math import make_val_and_grad_fn
  from tf_quant_finance.math import optimizer
  from tf_quant_finance.models.heston import approximations
label_desc: Use image node_id 1 for calling the _OptimizerArgHandler obj's underlying member method code with example usage: obj.get_initial_variance(packed_optimizer_args) and returns: _to_constrained