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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def leastsqbound(func, x0, bounds, args=(), **kw):
 """ Constrained multivariant Levenberg-Marquard optimization Minimize the sum of squares of a given function using the Levenberg-Marquard algorithm. Contraints on parameters are inforced using variable transformations as described in the MINUIT User's Guide by Fred James and Matthias Winkler. Parameters: * func functions to call for optimization. * x0 Starting estimate for the minimization. * bounds (min,max) pair for each element of x, defining the bounds on that parameter. Use None for one of min or max when there is no bound in that direction. * args Any extra arguments to func are places in this tuple. Returns: (x,{cov_x,infodict,mesg},ier) Return is described in the scipy.optimize.leastsq function. x and con_v are corrected to take into account the parameter transformation, infodic is not corrected. Additional keyword arguments are passed directly to the scipy.optimize.leastsq algorithm. """

    # check for full output
    if "full_output" in kw and kw["full_output"]:
        full = True
    else:
        full = False

    # convert x0 to internal variables
    i0 = external2internal(x0, bounds)

    # perfrom unconstrained optimization using internal variables
    r = leastsq(err, i0, args=(bounds, func, args), **kw)

    # unpack return convert to external variables and return
    if full:
        xi, cov_xi, infodic, mesg, ier = r
        xe = internal2external(xi, bounds)
        cov_xe = i2e_cov_x(xi, bounds, cov_xi)
        # XXX correct infodic 'fjac','ipvt', and 'qtf'
        return xe, cov_xe, infodic, mesg, ier

    else:
        xi, ier = r
        xe = internal2external(xi, bounds)
        return xe, ier