Project: s41467-024-52414-2 | Function: forward (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Segmentation-Through-Time UNet forward pass implementing longitudinal semantic segmentation by incorporating ConvLSTM cells between UNet encoder-decoder layers to explicitly track metastasis history across multiple timepoints for brain cancer detection

**KEY TERMS:** {
  "convlstm": "ConvLSTM3DCell or ConvLSTM2DCell for temporal feature processing",
  "hidden_state": "LSTM hidden state tracking tumor evolution across timepoints",
  "seq_len": "number of sequential MRI scans in longitudinal data",
  "x0-x4": "multi-scale encoded features from UNet encoder",
  "u4-u1": "decoder features with skip connections",
  "outputs": "segmentation predictions for each timepoint"
}

**CONTEXT CODE:**             >>> net = BasicUNet(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128), norm=("group", {"num_groups": 4}))

            # for spatial 3D
            >>> net = BasicUNet(spatial_dims=3, features=(32, 32, 64, 128, 256, 32))

        See Also

            - :py:class:`monai.networks.nets.DynUNet`
            - :py:class:`monai.networks.nets.UNet`

        """
        super().__init__()
        if dimensions is not None:
            spatial_dims = dimensions

        fea = ensure_tuple_rep(features, 6)

        self._check_kernel_size_consistency(kernel_size)

        if num_layers != 1:
            raise NotImplementedError

        self.kernel_size = kernel_size
        self.num_layers = num_layers
        self.spatial_dims = spatial_dims

        self.conv_0 = TwoConv(spatial_dims, in_channels, features[0], act, norm, bias, dropout)
        self.down_1 = Down(spatial_dims, fea[0], fea[1], act, norm, bias, dropout)
        self.down_2 = Down(spatial_dims, fea[1], fea[2], act, norm, bias, dropout)
        self.down_3 = Down(spatial_dims, fea[2], fea[3], act, norm, bias, dropout)
        self.down_4 = Down(spatial_dims, fea[3], fea[4], act, norm, bias, dropout)

        if spatial_dims == 3:
            self.convlstm = ConvLSTM3DCell(input_dim=fea[4],
                                           hidden_dim=fea[4],
                                           kernel_size=self.kernel_size,
                                           bias=bias)
        elif spatial_dims == 2:
            self.convlstm = ConvLSTM2DCell(input_dim=fea[4],
                                           hidden_dim=fea[4],
                                           kernel_size=self.kernel_size,
                                           bias=bias)

        self.upcat_4 = UpCat(spatial_dims, fea[4], fea[3], fea[3], act, norm, bias, dropout, upsample)
        self.upcat_3 = UpCat(spatial_dims, fea[3], fea[2], fea[2], act, norm, bias, dropout, upsample)
        self.upcat_2 = UpCat(spatial_dims, fea[2], fea[1], fea[1], act, norm, bias, dropout, upsample)
        self.upcat_1 = UpCat(spatial_dims, fea[1], fea[0], fea[5], act, norm, bias, dropout, upsample, halves=False)

        self.final_conv = Conv["conv", spatial_dims](fea[5], out_channels, kernel_size=1)

    def forward(self, x: torch.Tensor):
        """
        Args:
            x: input should have spatially N dimensions
                ``(Batch, in_channels, dim_0[, dim_1, ..., dim_N])``, N is defined by `dimensions`.
                It is recommended to have ``dim_n % 16 == 0`` to ensure all maxpooling inputs have
                even edge lengths.

        Returns:
            A torch Tensor of "raw" predictions in shape
            ``(Batch, out_channels, dim_0[, dim_1, ..., dim_N])``.
        """
        raise NotImplementedError("Function implementation has been masked")
    
    def _init_hidden(self, batch_size, image_size):
        init_states = []
        for i in range(self.num_layers):
            init_states.append(self.convlstm.init_hidden(batch_size, image_size))
        return init_states

    @staticmethod
    def _extend_for_multilayer(param, num_layers):
        if not isinstance(param, list):
            param = [param] * num_layers
        return param

    @staticmethod
    def _check_kernel_size_consistency(kernel_size):
        if not (isinstance(kernel_size, tuple) or
                (isinstance(kernel_size, list) and all([isinstance(elem, tuple) for elem in kernel_size]))):
            raise ValueError('`kernel_size` must be tuple or list of tuples')

Complete the function implementation replacing "raise NotImplementedError".