def __call__(self, x: Tensor, w: Tensor, b: Tensor):
        """Call special_conv2d_bias_activation with tensors x, w, b.

        Parameters
        ----------
        x : Tensor
            in shape (N, H, W, C_in)
        w : Tensor
            in shape (C_out, K_h, K_w, C_in)
        b : Tensor
            in shape (C_out)

        Returns
        -------
        List[Tensor]
            includes the output tensor in shape (N, H_out, W_out, C_out)
        """
        if self._auto_padding:
            last_dim = x._attrs["shape"][-1]._attrs["values"][0]
            if last_dim in range(1, 4):
                x = nhwc3to4()(x)
            elif last_dim in range(5, 8):
                x = nhwc3to8()(x)
        self._attrs["inputs"] = [x, w, b]
        self._set_depth()
        output_shape = self._infer_shapes(x, w)
        output = Tensor(output_shape, src_ops={self}, dtype=x._attrs["dtype"])
        self._extract_exec_path(x)
        self._extract_epilogue_alignment(output_shape)
        self._attrs["outputs"] = [output]
        return output
Call special_conv2d_bias_activation with tensors x, w, b.

        Parameters
        ----------
        x : Tensor
            in shape (N, H, W, C_in)
        w : Tensor
            in shape (C_out, K_h, K_w, C_in)
        b : Tensor
            in shape (C_out)

        Returns
        -------
        List[Tensor]
            includes the output tensor in shape (N, H_out, W_out, C_out)
        
python/aitemplate/compiler/ops/conv/special_conv2d_bias_activation.py