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.
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docstr: Build the symbolic graph to compute the Hessian-vector product.
  
  Args:
      f (tf.Tensor): The function whose Hessian needs to be computed.
      target (garage.tf.policies.Policy): A parameterized object to
          optimize over.
      inputs (tuple[tf.Tensor]): The inputs for function f.
      reg_coeff (float): A small value so that A -> A + reg*I.
      name (str): Name to be used in tf.name_scope.
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code: def update_hvp(self, f, target, inputs, reg_coeff, name=None):
      """Build the symbolic graph to compute the Hessian-vector product.
  
      Args:
          f (tf.Tensor): The function whose Hessian needs to be computed.
          target (garage.tf.policies.Policy): A parameterized object to
              optimize over.
          inputs (tuple[tf.Tensor]): The inputs for function f.
          reg_coeff (float): A small value so that A -> A + reg*I.
          name (str): Name to be used in tf.name_scope.
  
      """
  
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 2 for calling the HessianVectorProduct obj's underlying member method code with example usage: obj.update_hvp(f, target, inputs, reg_coeff, name) without return types