class_docstr: Bounds constraint on the variables.
  
  The constraint has the general inequality form::
  
      lb <= x <= ub
  
  It is possible to use equal bounds to represent an equality constraint or
  infinite bounds to represent a one-sided constraint.
  
  Parameters
  ----------
  lb, ub : array_like, optional
      Lower and upper bounds on independent variables. Each array must
      have the same size as x or be a scalar, in which case a bound will be
      the same for all the variables. Set components of `lb` and `ub` equal
      to fix a variable. Use ``np.inf`` with an appropriate sign to disable
      bounds on all or some variables. Note that you can mix constraints of
      different types: interval, one-sided or equality, by setting different
      components of `lb` and `ub` as necessary.
  keep_feasible : array_like of bool, optional
      Whether to keep the constraint components feasible throughout
      iterations. A single value set this property for all components.
      Default is False. Has no effect for equality constraints.
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code: def __init__(self, lb, ub, keep_feasible=False):
      self.lb = lb
      self.ub = ub
      self.keep_feasible = keep_feasible
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from ._hessian_update_strategy import BFGS
  from ._differentiable_functions import VectorFunction, LinearVectorFunction, IdentityVectorFunction
  from .optimize import OptimizeWarning
  from warnings import warn
  from scipy.sparse import issparse
label_desc: Use image node_id 1 to create a new Bounds object from inherited base classes: object with example: obj = Bounds(lb, ub, keep_feasible)