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 : dense array_like, optional
      Lower and upper bounds on independent variables. `lb`, `ub`, and
      `keep_feasible` must be the same shape or broadcastable.
      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. Defaults to ``lb = -np.inf``
      and ``ub = np.inf`` (no bounds).
  keep_feasible : dense array_like of bool, optional
      Whether to keep the constraint components feasible throughout
      iterations. Must be broadcastable with `lb` and `ub`.
      Default is False. Has no effect for equality constraints.
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code: def __repr__(self):
      start = f"{type(self).__name__}({self.lb!r}, {self.ub!r}"
      if np.any(self.keep_feasible):
          end = f", keep_feasible={self.keep_feasible!r})"
      else:
          end = ")"
      return start + end
  
imports: import numpy
  from ._hessian_update_strategy import BFGS
  from ._differentiable_functions import VectorFunction, LinearVectorFunction, IdentityVectorFunction
  from ._optimize import OptimizeWarning
  from warnings import warn, catch_warnings, simplefilter, filterwarnings
  from scipy.sparse import issparse
label_desc: Use image node_id 3 for calling the Bounds obj's underlying member method code with example usage: obj.__repr__() and returns: unknown