def merge_embedding_with_GO_labels(emb_df, GO_df):
    """Merges a gene_embedding with GO group presence df.

    Embedding cols are prefixed with emb_, while potential GO presence columns are prefixed with GO:

    Args:
        emb_df (pd.DataFrame): emb_df.index is gene_symbol
        GO_df (pd.DataFrame): GO_df.index is entrezgene

    Returns:
        (pd.DataFrame): Multi-index gene embedding with columns for GO presence concatenated.
    """
    # get df with gene_symbols and entrez_ids from fetal data (more updated than adult probes data)
    #all_genes = pd.read_csv('./data/raw/allen_human_fetal_brain/lmd_matrix_12566/rows_metadata.csv')
    #all_genes = all_genes[~((all_genes.gene_symbol.str.startswith('A_')) | (
        #all_genes.gene_symbol.str.startswith('CUST_')))].gene_symbol.drop_duplicates()

    #all_genes_w_entrez = utils.genesymbols_2_entrezids(all_genes)


    emb_df = emb_df.add_prefix('emb_')

    #df = emb_df.merge(all_genes_w_entrez, left_index=True,right_on='gene_symbol')

    emb_df = emb_df.rename(columns={"emb_gene_symbol": "gene_symbol", "emb_entrez_id": "entrez_id"})
    df = emb_df.merge(GO_df, left_on='entrez_id', right_index=True)

    return df.set_index(['entrez_id', 'gene_symbol'])