class_docstr: Represents the optimization result.
  
  Attributes
  ----------
  x : ndarray
      The solution of the optimization.
  success : bool
      Whether or not the optimizer exited successfully.
  status : int
      Termination status of the optimizer. Its value depends on the
      underlying solver. Refer to `message` for details.
  message : str
      Description of the cause of the termination.
  fun, jac, hess: ndarray
      Values of objective function, its Jacobian and its Hessian (if
      available). The Hessians may be approximations, see the documentation
      of the function in question.
  hess_inv : object
      Inverse of the objective function's Hessian; may be an approximation.
      Not available for all solvers. The type of this attribute may be
      either np.ndarray or scipy.sparse.linalg.LinearOperator.
  nfev, njev, nhev : int
      Number of evaluations of the objective functions and of its
      Jacobian and Hessian.
  nit : int
      Number of iterations performed by the optimizer.
  maxcv : float
      The maximum constraint violation.
  
  Notes
  -----
  There may be additional attributes not listed above depending of the
  specific solver. Since this class is essentially a subclass of dict
  with attribute accessors, one can see which attributes are available
  using the `keys()` method.
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code: def __repr__(self):
      if self.keys():
          m = max(map(len, list(self.keys()))) + 1
          return "\n".join(
              [
                  k.rjust(m) + ": " + repr(v)
                  for k, v in sorted(self.items())
              ]
          )
      else:
          return self.__class__.__name__ + "()"
  
imports: import warnings
  import sys
  from numpy import atleast_1d, eye, argmin, zeros, shape, squeeze, asarray, sqrt, Inf, asfarray, isinf
  import numpy
  from .linesearch import line_search_wolfe1, line_search_wolfe2, line_search_wolfe2, LineSearchWarning
  from ._numdiff import approx_derivative
  from scipy._lib._util import getfullargspec_no_self
  from scipy._lib._util import MapWrapper
  from scipy.optimize._differentiable_functions import ScalarFunction, FD_METHODS
label_desc: Use image node_id 2 for calling the OptimizeResult obj's underlying member method code with example usage: obj.__repr__() and returns: str, unknown