Project: 048_Topological_Blindspots_Understanding_and_Extending | Function: TensorDiagram._apply_homp_layer (iclr)

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:** Core tensor diagram execution framework that unifies HOMP and SMCN architectures by applying layer-wise updates across multi-rank cellular complexes with optional residual connections

**KEY TERMS:** {
  "layer": "HOMP/SMCN layer dictionary",
  "output_rank": "target cellular rank",
  "dropout_layer": "regularization layer",
  "residual": "residual connection flag",
  "setattr": "dynamic attribute assignment for cell features"
}

**CONTEXT CODE:**             self.layers, self.dropouts, self.residuals
        ):
            data = TensorDiagram._apply_homp_layer(
                data=data, layer=layer, dropout_layer=dropout_layer, residual=residual
            )

        out = []
        # Aggregate over cell features
        for rank in self.output_ranks:
            cell_features = getattr(data, f"x_{rank}")
            batch_index = torch.squeeze(getattr(data, f"x_{rank}_batch")).type(
                torch.int64
            )

            # In case of empty cells, pad final feature per complex so each complex has
            # one vector representation per rank
            batch_max_nonempty = (
                0 if batch_index.numel() == 0 else batch_index.max().item()
            )
            if batch_max_nonempty < batch_size - 1:
                batch_index = torch.concat(
                    [
                        batch_index,
                        torch.arange(batch_max_nonempty + 1, batch_size).to(
                            self.device
                        ),
                    ]
                )
                cell_features = torch.concat(
                    [
                        cell_features,
                        torch.zeros(
                            batch_size - batch_max_nonempty - 1, cell_features.shape[-1]
                        ).to(self.device),
                    ],
                    dim=0,
                )

            out.append(self.aggregation(cell_features, batch_index))

        if self.zinc_head:
            return self.head(torch.cat(out, dim=1))
        else:
            out = [self.linear_heads[i](out[i]) for i in range(len(self.output_ranks))]
            x_out = torch.sum(torch.stack(out, dim=0), dim=0)
            x_out = self.final_dropout(x_out)
            x_out = self.final_linear(x_out)
            return x_out

    @staticmethod
    def _apply_homp_layer(
        data: Data, layer: Layer, dropout_layer, residual: bool
    ) -> Data:
        raise NotImplementedError("Function implementation has been masked for testing")
    

    def _get_head(
        self, output_ranks: List[int], embed_dim: int, output_dim: int, dropout
    ):
        num_ranks = len(output_ranks)
        return Sequential(
            Linear(num_ranks * embed_dim, embed_dim),
            nn.BatchNorm1d(embed_dim),
            ReLU(),
            nn.Dropout(p=dropout),
            Linear(embed_dim, 2 * embed_dim),
            nn.BatchNorm1d(2 * embed_dim),
            ReLU(),
            Linear(2 * embed_dim, output_dim),
        )


Complete the function implementation replacing "raise NotImplementedError".