parameters: (ys,
                 xs,
                 gate_gradients=False,
                 aggregation_method=None,
                 name="hessians")
return_statement: return hessians(
        ys,
        xs,
        name=name,
        colocate_gradients_with_ops=True,
        gate_gradients=gate_gradients,
        aggregation_method=aggregation_method)
docstring: Constructs the Hessian of sum of `ys` with respect to `x` in `xs`.
  
    `hessians()` adds ops to the graph to output the Hessian matrix of `ys`
    with respect to `xs`.  It returns a list of `Tensor` of length `len(xs)`
    where each tensor is the Hessian of `sum(ys)`.
  
    The Hessian is a matrix of second-order partial derivatives of a scalar
    tensor (see https://en.wikipedia.org/wiki/Hessian_matrix for more details).
  
    Args:
      ys: A `Tensor` or list of tensors to be differentiated.
      xs: A `Tensor` or list of tensors to be used for differentiation.
      gate_gradients: See `gradients()` documentation for details.
      aggregation_method: See `gradients()` documentation for details.
      name: Optional name to use for grouping all the gradient ops together.
        defaults to 'hessians'.
  
    Returns:
      A list of Hessian matrices of `sum(ys)` for each `x` in `xs`.
  
    Raises:
      LookupError: if one of the operations between `xs` and `ys` does not
        have a registered gradient function.
docstring_summary: Constructs the Hessian of sum of `ys` with respect to `x` in `xs`.
docstring_tokens: Constructs the Hessian of sum of ys with respect to x in xs .
function: def HessiansV2(ys,
                 xs,
                 gate_gradients=False,
                 aggregation_method=None,
                 name="hessians"):
    """Constructs the Hessian of sum of `ys` with respect to `x` in `xs`.
  
    `hessians()` adds ops to the graph to output the Hessian matrix of `ys`
    with respect to `xs`.  It returns a list of `Tensor` of length `len(xs)`
    where each tensor is the Hessian of `sum(ys)`.
  
    The Hessian is a matrix of second-order partial derivatives of a scalar
    tensor (see https://en.wikipedia.org/wiki/Hessian_matrix for more details).
  
    Args:
      ys: A `Tensor` or list of tensors to be differentiated.
      xs: A `Tensor` or list of tensors to be used for differentiation.
      gate_gradients: See `gradients()` documentation for details.
      aggregation_method: See `gradients()` documentation for details.
      name: Optional name to use for grouping all the gradient ops together.
        defaults to 'hessians'.
  
    Returns:
      A list of Hessian matrices of `sum(ys)` for each `x` in `xs`.
  
    Raises:
      LookupError: if one of the operations between `xs` and `ys` does not
        have a registered gradient function.
    """
    return hessians(
        ys,
        xs,
        name=name,
        colocate_gradients_with_ops=True,
        gate_gradients=gate_gradients,
        aggregation_method=aggregation_method)
function_tokens: def HessiansV2 ( ys , xs , gate_gradients = False , aggregation_method = None , name = "hessians" ) : return hessians ( ys , xs , name = name , colocate_gradients_with_ops = True , gate_gradients = gate_gradients , aggregation_method = aggregation_method )