def _call()

in core/maxframe/tensor/arithmetic/core.py [0:0]

• 48 lines of code
• 15 McCabe index (conditional complexity)

    def _call(self, x, out1=None, out2=None, out=None, where=None):
        dtype = [r.dtype for r in self._fun(np.empty(1, dtype=x.dtype))]

        out = out or (None, None)
        out1 = out1 or out[0]
        out2 = out2 or out[1]
        x, out1, out2, where = self._process_inputs(x, out1, out2, where)
        shape = x.shape
        order1 = self._calc_order(x, out1)
        order2 = self._calc_order(x, out2)

        inputs = filter_inputs([x, out1, out2, where])
        t1, t2 = self.new_tensors(
            inputs,
            shape,
            kws=[
                {"order": order1, "dtype": dtype[0], "side": "left"},
                {"order": order2, "dtype": dtype[1], "side": "right"},
            ],
        )

        if out1 is None and out2 is None:
            return ExecutableTuple([t1, t2])

        if out1 is not None:
            check_out_param(out1, t1, self.casting)
            out1_shape, out1_dtype = out1.shape, out1.dtype
        else:
            out1_shape, out1_dtype = t1.shape, t1.dtype
        if out2 is not None:
            check_out_param(out2, t2, self.casting)
            out2_shape, out2_dtype = out2.shape, out2.dtype
        else:
            out2_shape, out2_dtype = t2.shape, t2.dtype
        # if `out` is specified, use out's dtype and shape
        if t1.shape != out1_shape or t2.shape != out2_shape:
            t1, t2 = self.new_tensor(
                inputs,
                [out1_shape, out2_shape],
                kws=[
                    {"order": order1, "dtype": out1_dtype},
                    {"order": order2, "dtype": out2_dtype},
                ],
            )

        if out1 is not None:
            out1.data = t1.data
        else:
            out1 = t1
        if out2 is not None:
            out2.data = t2.data
        else:
            out2 = t2
        return ExecutableTuple([out1, out2])