class_docstr: Penalized Limited-memory BFGS (L-BFGS) optimizer.
  
  Performs constrained optimization via penalized L-BFGS. The penalty term is
  adaptively adjusted to make sure that the constraint is satisfied.
  
  Args:
      max_opt_itr (int): Maximum iteration for update.
      initial_penalty (float): Initial penalty.
      min_penalty (float): Minimum penalty allowed. Penalty will be clipped
          if lower than this value.
      max_penalty (float): Maximum penalty allowed. Penalty will be clipped
          if higher than this value.
      increase_penalty_factor (float): Factor to increase penalty in each
          penalty iteration.
      decrease_penalty_factor (float): Factor to decrease penalty in each
          penalty iteration.
      max_penalty_itr (int): Maximum penalty iterations to perform.
      adapt_penalty (bool): Whether the penalty is adaptive or not. If false,
          penalty will not change.
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docstr: Object.__getstate__.
  
  Returns:
      dict: The state to be pickled for the instance.
docstr_tok: ["Object.__getstate__",".","Returns",":","dict",":","The","state","to","be","pickled","for","the","instance","."]
code: def __getstate__(self):
      """Object.__getstate__.
  
      Returns:
          dict: The state to be pickled for the instance.
  
      """
      new_dict = self.__dict__.copy()
      del new_dict["_opt_fun"]
      return new_dict
  
file_path: garage/src/garage/tf/optimizers/penalty_lbfgs_optimizer.py
imports: from dowel import logger
  import numpy
  import scipy.optimize
  import tensorflow
  from garage.tf import compile_function, flatten_tensor_variables
  from garage.tf.optimizers._dtypes import LazyDict
label_desc: Use image node_id 6 for calling the PenaltyLBFGSOptimizer obj's underlying member method code with example usage: obj.__getstate__() and returns: new_dict