class_docstr: Computes Hessian-vector product using finite difference method.
  
  Args:
      base_eps (float): Base epsilon value.
      symmetric (bool): Symmetric or not.
      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: ["Computes","Hessian-vector","product","using","finite","difference","method",".","Args",":","base_eps","(","float",")",":","Base","epsilon","value",".","symmetric","(","bool",")",":","Symmetric","or","not",".","num_slices","(","int",")",":","Hessian-vector","product","function","'s","inputs","will","be","divided","into","num_slices","and","then","averaged","together","to","improve","performance","."]
code: def __init__(self, base_eps=1e-8, symmetric=True, num_slices=1):
      super().__init__(num_slices=num_slices)
      self.base_eps = base_eps
      self.symmetric = symmetric
  
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 1 to create a new FiniteDifferenceHVP object from inherited base classes: HessianVectorProduct with example: obj = FiniteDifferenceHVP(base_eps, symmetric, num_slices)