Project: s41467-024-50317-w | 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:** Implements the core RNAPath multiple instance learning model that predicts RNA expression levels from histology tile representations using parallel linear regressors and mean pooling aggregation

**KEY TERMS:** {
  "h": "histology tile representations (384-dim)",
  "classifiers": "N parallel linear regressors for N genes",
  "patch_logits": "tile-level expression predictions",
  "logits": "whole-tissue expression via mean pooling",
  "genes_list": "subset of genes for memory efficiency"
}

**CONTEXT CODE:** import torch
import torch.nn as nn
from utils.utils import initialize_weights
import numpy as np



class RNAPath(nn.Module):
    def __init__(self, n_classes=0):
        super(RNAPath, self).__init__()
        
        """
            Model initialisation requires:
            - number of classes (number of genes (N))
            We train N linear regressors, one for each gene.
            
        """
        self.n_classes = n_classes
        self.dropout = torch.nn.Dropout(p=0.1)
        classifiers = [nn.Linear(384, 1) for i in range(self.n_classes)]
        self.classifiers = nn.ModuleList(classifiers)


        initialize_weights(self)

    def relocate(self):
        """
            Move model to device
        """
        device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.classifiers = self.classifiers.to(device)
        

    def forward(self, h, genes_list = None, return_patch_expression = False):
        """"
            gene_list: list of indexes of genes; the full list of genes is [0, #genes)
            return_patch_expression: whether to return patch-level scores; patch logits are reported as a matrix
            of shape (K, N), where K is the number of patches and N the number of genes

            Due to memory limitation, we just train on a subset of genes, therefore we split the genes in batches
            and we provide a list of indexes at each iteration; during inference, this is not required, therefore we
            regress the expression of all the genes of interest at the same time.

        """
        # MASKED: Implements the core RNAPath multiple instance learning model that predicts RNA expression levels from histology tile representations using parallel linear regressors and mean pooling aggregation
        raise NotImplementedError("Function has been masked for testing purposes")





Complete the function implementation replacing "raise NotImplementedError".