diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 50b4cb9..992553c 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.9"] # TODO: re-add 3.10, 3.11
       fail-fast: false
 
     steps:
diff --git a/pytorch_finufft/functional.py b/pytorch_finufft/functional.py
index 69c763b..cce4635 100644
--- a/pytorch_finufft/functional.py
+++ b/pytorch_finufft/functional.py
@@ -200,114 +200,6 @@ def backward(
         return grad_points, grad_targets, None, None, None
 
 
-class _finufft1D3(torch.autograd.Function):
-    """
-    FINUFFT 1d Problem type 3
-    """
-
-    @staticmethod
-    def forward(
-        ctx: Any,
-        points: torch.Tensor,
-        values: torch.Tensor,
-        targets: torch.Tensor,
-        out: Optional[torch.Tensor] = None,
-        finufftkwargs: Dict[str, Union[int, float]] = {},
-    ) -> torch.Tensor:
-        """
-        Evaluates the Type 3 NUFFT on the inputs.
-
-        ```
-            M-1
-        f[k] = SUM c[j] exp(+/-i s[k] x[j]),
-            j=0
-
-            for k = 0, ..., N-1
-        ```
-
-        Parameters
-        ----------
-        ctx : Any
-            PyTorch context object
-        points : torch.Tensor
-            The non-uniform points x_j.
-        values : torch.Tensor
-            The source strengths c_j.
-        targets : torch.Tensor
-            The non-uniform target points s_k.
-        out : Optional[torch.Tensor]
-            Array to populate with result in-place, by default None
-        finufftkwargs : Dict[str, Union[int, float]]
-            Additional arguments will be passed into FINUFFT. See
-            https://finufft.readthedocs.io/en/latest/python.html. By default
-            an empty dictionary
-
-        Returns
-        -------
-        torch.Tensor
-            The resultant array f[k]
-        """
-
-        if out is not None:
-            print("In-place results are not yet implemented")
-
-        err._type3_checks((points,), values, (targets,))
-
-        # NB: no mode ordering kwarg for type 3
-        finufftkwargs = {k: v for k, v in finufftkwargs.items()}
-        _i_sign = finufftkwargs.pop("isign", -1)
-
-        # NOTE: this is passed in as None in the test suite
-        if ctx is not None:
-            ctx.isign = _i_sign
-            ctx.finufftkwargs = finufftkwargs
-
-        finufft_out = finufft.nufft1d3(
-            points.data.numpy(),
-            values.data.numpy(),
-            targets.data.numpy(),
-            isign=_i_sign,
-            **finufftkwargs,
-        )
-
-        return torch.from_numpy(finufft_out)
-
-    @staticmethod
-    def backward(
-        ctx: Any, grad_output: torch.Tensor
-    ) -> Tuple[Union[torch.Tensor, Any], ...]:
-        """
-        Implements gradients for backward mode automatic differentation
-
-        Parameters
-        ----------
-        ctx : Any
-            PyTorch context object
-        grad_output : torch.Tensor
-            Backpass gradient output
-
-        Returns
-        -------
-        Tuple[Union[torch.Tensor, Any], ...]
-            Tuple of derivatives with respect to each input
-        """
-        _i_sign = ctx.isign
-        _mode_ordering = ctx.mode_ordering
-        _fftshift = ctx.fftshift
-        _finufftkwargs = ctx.finufftkwargs
-
-        grad_points = grad_values = grad_targets = None
-
-        if ctx.needs_input_grad[0]:
-            grad_points = None
-        if ctx.needs_input_grad[1]:
-            grad_values = None
-        if ctx.needs_input_grad[2]:
-            grad_targets = None
-
-        return grad_points, grad_values, grad_targets, None, None, None
-
-
 ###############################################################################
 # 2d Functions
 ###############################################################################
@@ -540,77 +432,6 @@ def backward(
         )
 
 
-class _finufft2D3(torch.autograd.Function):
-    """
-    FINUFFT 2D problem type 3
-    """
-
-    @staticmethod
-    def forward(
-        ctx: Any,
-        points_x: torch.Tensor,
-        points_y: torch.Tensor,
-        values: torch.Tensor,
-        targets_s: torch.Tensor,
-        targets_t: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-        Evaluates the Type 3 NUFFT on the inputs
-
-        Parameters
-        ----------
-        ctx : Any
-            _description_
-        points_x : torch.Tensor
-            _description_
-        points_y : torch.Tensor
-            _description_
-        values : torch.Tensor
-            _description_
-        targets_s : torch.Tensor
-            _description_
-        targets_t : torch.Tensor
-            _description_
-
-        Returns
-        -------
-        torch.Tensor
-            _description_
-
-        Raises
-        ------
-        ValueError
-            _description_
-        """
-
-        if True:
-            raise ValueError("2D3 is not implemented yet")
-
-        return torch.ones(10)
-
-    @staticmethod
-    def backward(
-        ctx: Any, grad_output: torch.Tensor
-    ) -> Tuple[Union[torch.Tensor, None], ...]:
-        """
-        Implements gradients for backward mode automatic differentiation
-
-        Parameters
-        ----------
-        ctx : Any
-            TODO PyTorch context object
-        grad_output : torch.Tensor
-            TODO VJP output
-
-        Returns
-        -------
-        Tuple[Union[torch.Tensor, None], ...]
-            Tuple of derivatives with respect to each input
-        """
-
-        return None, None, None, None, None
-
-
 ###############################################################################
 # 3d Functions
 ###############################################################################
@@ -866,106 +687,6 @@ def backward(
         )
 
 
-class _finufft3D3(torch.autograd.Function):
-    """
-    FINUFFT 3D problem type 3
-    """
-
-    @staticmethod
-    def forward(
-        ctx: Any,
-        points_x: torch.Tensor,
-        points_y: torch.Tensor,
-        points_z: torch.Tensor,
-        values: torch.Tensor,
-        targets_s: torch.Tensor,
-        targets_t: torch.Tensor,
-        targets_u: torch.Tensor,
-        out: Optional[torch.Tensor] = None,
-        **finufftkwargs: Union[int, float],
-    ) -> torch.Tensor:
-        """
-        TODO Description here!
-
-        Parameters
-        ----------
-        ctx : Any
-            PyTorch context object
-        points_x : torch.Tensor
-            The nonuniform source points x_j
-        points_y : torch.Tensor
-            The nonuniform source points y_j
-        points_z : torch.Tensor
-            The nonuniform source points z_j
-        values : torch.Tensor
-            The source strengths c_j
-        targets_s : torch.Tensor
-            The target Fourier mode coefficients s_k
-        targets_t : torch.Tensor
-            The target Fourier mode coefficients t_k
-        targets_u : torch.Tensor
-            The target Fourier mode coefficients u_k
-        out : Optional[torch.Tensor], optional
-            Array to take the result in-place, by default None
-        **finufftkwargs : Union[int, float]
-            Additional arguments will be passed into FINUFFT. See
-            https://finufft.readthedocs.io/en/latest/python.html
-
-        Returns
-        -------
-        torch.Tensor
-            The resultant array f[k]
-
-        Raises
-        ------
-        ValueError
-            _description_
-        """
-
-        if True:
-            raise ValueError("3D3 is not implemented yet")
-
-        return torch.ones(10)
-
-    @staticmethod
-    def backward(
-        ctx: Any, grad_output: torch.Tensor
-    ) -> Tuple[Union[torch.Tensor, None], ...]:
-        """
-        Implements gradients for backward mode automatic differentiation
-
-        Parameters
-        ----------
-        ctx : Any
-            TODO PyTorch context object
-        grad_output : torch.Tensor
-            TODO VJP output
-
-        Returns
-        -------
-        Tuple[Union[torch.Tensor, None], ...]
-            Tuple of derivatives with respect to each input
-        """
-
-        grad_points_x = grad_points_y = grad_points_z = None
-
-        grad_values = None
-
-        grad_targets_s = grad_targets_t = grad_targets_u = None
-
-        return (
-            grad_points_x,
-            grad_points_y,
-            grad_points_z,
-            grad_values,
-            grad_targets_s,
-            grad_targets_t,
-            grad_targets_u,
-            None,
-            None,
-        )
-
-
 ###############################################################################
 # Consolidated forward function for all 1D, 2D, and 3D problems for nufft type 1
 ###############################################################################