def _doprint_loops(self, expr, assign_to=None):
        # Here we print an expression that contains Indexed objects, they
        # correspond to arrays in the generated code.  The low-level implementation
        # involves looping over array elements and possibly storing results in temporary
        # variables or accumulate it in the assign_to object.

        if self._settings.get('contract', True):
            from sympy.tensor import get_contraction_structure
            # Setup loops over non-dummy indices  --  all terms need these
            indices = self._get_expression_indices(expr, assign_to)
            # Setup loops over dummy indices  --  each term needs separate treatment
            dummies = get_contraction_structure(expr)
        else:
            indices = []
            dummies = {None: (expr,)}
        openloop, closeloop = self._get_loop_opening_ending(indices)

        # terms with no summations first
        if None in dummies:
            text = StrPrinter.doprint(self, Add(*dummies[None]))
        else:
            # If all terms have summations we must initialize array to Zero
            text = StrPrinter.doprint(self, 0)

        # skip redundant assignments (where lhs == rhs)
        lhs_printed = self._print(assign_to)
        lines = []
        if text != lhs_printed:
            lines.extend(openloop)
            if assign_to is not None:
                text = self._get_statement((lhs_printed, text) % "%s = %s")
            lines.append(text)
            lines.extend(closeloop)

        # then terms with summations
        for d in dummies:
            if isinstance(d, tuple):
                indices = self._sort_optimized(d, expr)
                openloop_d, closeloop_d = self._get_loop_opening_ending(
                    indices)

                for term in dummies[d]:
                    if term in dummies and not ([list(f.keys()) for f in dummies[term]]
                            == [[None] for f in dummies[term]]):
                        # If one factor in the term has it's own internal
                        # contractions, those must be computed first.
                        # (temporary variables?)
                        raise NotImplementedError(
                            "FIXME: no support for contractions in factor yet")
                    else:

                        # We need the lhs expression as an accumulator for
                        # the loops, i.e
                        #
                        # for (int d=0; d < dim; d++){
                        #    lhs[] = lhs[] + term[][d]
                        # }           ^.................. the accumulator
                        #
                        # We check if the expression already contains the
                        # lhs, and raise an exception if it does, as that
                        # syntax is currently undefined.  FIXME: What would be
                        # a good interpretation?
                        if assign_to is None:
                            raise AssignmentError(
                                "need assignment variable for loops")
                        if term.has(assign_to):
                            raise ValueError("FIXME: lhs present in rhs,\
                                this is undefined in CodePrinter")

                        lines.extend(openloop)
                        lines.extend(openloop_d)
                        text = "%s = %s" % (lhs_printed, StrPrinter.doprint(
                            self, assign_to + term))
                        lines.append(self._get_statement(text))
                        lines.extend(closeloop_d)
                        lines.extend(closeloop)

        return "\n".join(lines)
dataset/ETHPy150Open sympy/sympy/sympy/printing/codeprinter.py/CodePrinter._doprint_loops/swapped operands of `%`