How to use the kipoi.utils.read_txt function in kipoi

def parse_context(orig_dir):
    """Parses the context for each model
    """
    bigwig = read_txt(os.path.join(orig_dir, "bigwig.txt"))
    tasks = read_txt(os.path.join(orig_dir, "chip.txt"))
    features = read_txt(os.path.join(orig_dir, "feature.txt"))
    meta_fname = os.path.join(orig_dir, "meta.txt")
    if os.path.exists(meta_fname):
        meta = read_txt(meta_fname)
        n_meta_features = len(meta)
        assert n_meta_features == 8
    else:
        meta = None
        n_meta_features = 0

    needs_gencode = "gencode" in features
    if needs_gencode:
        n_meta_features += 6
    seq_n_channels = 4 + len(bigwig)

def parse_context(orig_dir):
    """Parses the context for each model
    """
    bigwig = read_txt(os.path.join(orig_dir, "bigwig.txt"))
    tasks = read_txt(os.path.join(orig_dir, "chip.txt"))
    features = read_txt(os.path.join(orig_dir, "feature.txt"))
    meta_fname = os.path.join(orig_dir, "meta.txt")
    if os.path.exists(meta_fname):
        meta = read_txt(meta_fname)
        n_meta_features = len(meta)
        assert n_meta_features == 8
    else:
        meta = None
        n_meta_features = 0

    needs_gencode = "gencode" in features
    if needs_gencode:
        n_meta_features += 6
    seq_n_channels = 4 + len(bigwig)

    return {"bigwig": bigwig,
            "tasks": tasks,

tmpdir='/tmp/KipoiSplice/'):
    """
    Args:
      vcf_file: Path to the input vcf file
      fasta_file: reference genome fasta file
      gtf_file: path to the GTF file required by the models (Ensemble)

      batch_size: batch size to use with all the models
      num_workers: number of workers to use for each model
      tmpdir (optional): path to the temporary directory where to store the predictions
    """
    #  contains_conservation: if True, include the conservation scores in the inputs
    #     NOTE: This requires the `vcf_file` to be annotated using VEP
    contains_conservation=False
    MODELS = ["MaxEntScan/3prime", "MaxEntScan/5prime", "HAL", "labranchor"]
    features = read_txt(os.path.join(this_path, "features.txt"))

    # Could also be generated on the fly from "MODELS"
    with open(os.path.join(this_path, "model_table_cols.json"), "r") as ifh:
        model_output_col_names = json.load(ifh)

    os.makedirs(tmpdir, exist_ok=True)
    tmpdir = tempfile.mkdtemp(dir=tmpdir)

    # Generate a vcf file for each model
    for model in MODELS:
        # One could even parallelize here using joblib for example
        out_vcf_fpath = os.path.join(tmpdir, model + ".vcf")
        ensure_dirs(out_vcf_fpath)
        dataloader_arguments = {"gtf_file": os.path.abspath(gtf_file),
                                "fasta_file": os.path.abspath(fasta_file)}
        if "rbp_eclip" in model:

def parse_context(orig_dir):
    """Parses the context for each model
    """
    bigwig = read_txt(os.path.join(orig_dir, "bigwig.txt"))
    tasks = read_txt(os.path.join(orig_dir, "chip.txt"))
    features = read_txt(os.path.join(orig_dir, "feature.txt"))
    meta_fname = os.path.join(orig_dir, "meta.txt")
    if os.path.exists(meta_fname):
        meta = read_txt(meta_fname)
        n_meta_features = len(meta)
        assert n_meta_features == 8
    else:
        meta = None
        n_meta_features = 0

    needs_gencode = "gencode" in features
    if needs_gencode:
        n_meta_features += 6
    seq_n_channels = 4 + len(bigwig)

    return {"bigwig": bigwig,
            "tasks": tasks,
            "features": features,
            "meta": meta,
            "needs_mappability": "Unique35" in bigwig,

def parse_context(orig_dir):
    """Parses the context for each model
    """
    bigwig = read_txt(os.path.join(orig_dir, "bigwig.txt"))
    tasks = read_txt(os.path.join(orig_dir, "chip.txt"))
    features = read_txt(os.path.join(orig_dir, "feature.txt"))
    meta_fname = os.path.join(orig_dir, "meta.txt")
    if os.path.exists(meta_fname):
        meta = read_txt(meta_fname)
        n_meta_features = len(meta)
        assert n_meta_features == 8
    else:
        meta = None
        n_meta_features = 0

    needs_gencode = "gencode" in features
    if needs_gencode:
        n_meta_features += 6
    seq_n_channels = 4 + len(bigwig)

    return {"bigwig": bigwig,