Project: s41467-024-48567-9 | Function: CFConv.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:** Continuous-filter convolution block that implements the core SchNet convolution operation. Takes atomic embeddings and interatomic distances, applies learned filters through continuous convolution, and aggregates neighbor contributions.

**KEY TERMS:** {
  "CFConv": "continuous-filter convolution",
  "filter_network": "filter-generating networks",
  "W": "learned filters",
  "cutoff_network": "cutoff function"
}

**CONTEXT CODE:** import torch
from torch import nn

from SchNet4AIM.nn import Dense
from SchNet4AIM.nn.base import Aggregate


__all__ = ["CFConv"]


class CFConv(nn.Module):
    r"""Continuous-filter convolution block used in SchNet module.

    Args:
        n_in (int): number of input (i.e. atomic embedding) dimensions.
        n_filters (int): number of filter dimensions.
        n_out (int): number of output dimensions.
        filter_network (nn.Module): filter block.
        cutoff_network (nn.Module, optional): if None, no cut off function is used.
        activation (callable, optional): if None, no activation function is used.
        normalize_filter (bool, optional): If True, normalize filter to the number
            of neighbors when aggregating.
        axis (int, optional): axis over which convolution should be applied.

    """

    def __init__(
        self,
        n_in,
        n_filters,
        n_out,
        filter_network,
        cutoff_network=None,
        activation=None,
        normalize_filter=False,
        axis=2,
    ):
        super(CFConv, self).__init__()
        self.in2f = Dense(n_in, n_filters, bias=False, activation=None)
        self.f2out = Dense(n_filters, n_out, bias=True, activation=activation)
        self.filter_network = filter_network
        self.cutoff_network = cutoff_network
        self.agg = Aggregate(axis=axis, mean=normalize_filter)

    def forward(self, x, r_ij, neighbors, pairwise_mask, f_ij=None):
        """Compute convolution block.

        Args:
            x (torch.Tensor): input representation/embedding of atomic environments
                with (N_b, N_a, n_in) shape.
            r_ij (torch.Tensor): interatomic distances of (N_b, N_a, N_nbh) shape.
            neighbors (torch.Tensor): indices of neighbors of (N_b, N_a, N_nbh) shape.
            pairwise_mask (torch.Tensor): mask to filter out non-existing neighbors
                introduced via padding.
            f_ij (torch.Tensor, optional): expanded interatomic distances in a basis.
                If None, r_ij.unsqueeze(-1) is used.

        Returns:
            torch.Tensor: block output with (N_b, N_a, n_out) shape.

        """
        raise NotImplementedError("Function has been masked for testing purposes")


Complete the function implementation replacing "raise NotImplementedError".