code: def from_callback(cls, cb, transf_cbs, nx, nparams=0, pre_adj=None,
                        **kwargs):
          
          be = Backend(kwargs.pop('backend', None))
          x = be.real_symarray('x', nx)
          p = be.real_symarray('p', nparams)
          try:
              transf = [(transf_cbs[idx][0](xi),
                         transf_cbs[idx][1](xi))
                        for idx, xi in enumerate(x)]
          except TypeError:
              transf = zip(_map2(transf_cbs[0], x), _map2(transf_cbs[1], x))
          try:
              exprs = cb(x, p, be)
          except TypeError:
              exprs = _ensure_3args(cb)(x, p, be)
          return cls(x, _map2l(pre_adj, exprs), transf, p, backend=be, **kwargs)
docs: Generate a TransformedSys instance from a callback
  
          Parameters
          ----------
          cb : callable
              Should have the signature ``cb(x, p, backend) -> list of exprs``.
              The callback ``cb`` should return *untransformed* expressions.
          transf_cbs : pair or iterable of pairs of callables
              Callables for forward- and backward-transformations. Each
              callable should take a single parameter (expression) and
              return a single expression.
          nx : int
              Number of unkowns.
          nparams : int
              Number of parameters.
          pre_adj : callable, optional
              To tweak expression prior to transformation. Takes a
              sinlge argument (expression) and return a single argument
              rewritten expression.
          \\*\\*kwargs :
              Keyword arguments passed on to :class:`TransformedSys`. See also
              :class:`SymbolicSys` and :class:`pyneqsys.NeqSys`.
  
          Examples
          --------
          >>> import sympy as sp
          >>> transformed = TransformedSys.from_callback(lambda x, p, be: [
          ...     x[0]*x[1] - p[0],
          ...     be.exp(-x[0]) + be.exp(-x[1]) - p[0]**-2
          ... ], (sp.log, sp.exp), 2, 1)
          ...