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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def filter_out_mutations_in_normal(tumordf, normaldf, most_common_maf_min=0.2, most_common_count_maf_threshold=20, most_common_count_min=1):
 """Remove mutations that are in normal"""

    df = tumordf.merge(normaldf, on=["chrom", "pos"], suffixes=("_T", "_N"))

    # filters
    common_al = (df.most_common_al_count_T == df.most_common_count_T) & (df.most_common_al_T == df.most_common_al_N)
    common_indel = (df.most_common_indel_count_T == df.most_common_count_T) & \
        (df.most_common_indel_T == df.imost_common_indel_N)
    normal_criteria = ((df.most_common_count_N >= most_common_count_maf_threshold) &
                       (df.most_common_maf_N > most_common_maf_min)) | \
        ((df.most_common_count_N < most_common_count_maf_threshold) &
         (df.most_common_count_N > most_common_count_min))
    df = df[~(common_al | common_indel) & normal_criteria]

    # restore column names of tumor
    for c in df.columns:
        if c.endswith("_N"):
            del df[c]
            df.columns = [c[:-2] if c.endswith("_T") else c for c in df.columns]

    return df