class_docstr: Base class for computing Hessian-vector product.
  
  Args:
      num_slices (int): Hessian-vector product function's inputs will be
          divided into num_slices and then averaged together to improve
          performance.
class_docstr_tok: ["Base","class","for","computing","Hessian-vector","product",".","Args",":","num_slices","(","int",")",":","Hessian-vector","product","function","'s","inputs","will","be","divided","into","num_slices","and","then","averaged","together","to","improve","performance","."]
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["_hvp_fun"]
      return new_dict
  
imports: import abc
  from dowel import logger
  import numpy
  import tensorflow
  from garage.tf import compile_function, flatten_tensor_variables, new_tensor_like
  from garage.tf.optimizers._dtypes import LazyDict
label_desc: Use image node_id 4 for calling the HessianVectorProduct obj's underlying member method code with example usage: obj.__getstate__() and returns: new_dict