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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 read_umi_tools(filename: PathLike, dtype: str='float32') -> AnnData: """Read a gzipped condensed count matrix from umi_tools. Parameters filename File name to read from. """

    # import pandas for conversion of a dict of dicts into a matrix
    # import gzip to read a gzipped file :-)
    import gzip
    from pandas import DataFrame

    dod = {}  # this will contain basically everything
    fh = gzip.open(fspath(filename))
    header = fh.readline()  # read the first line

    for line in fh:
        t = line.decode('ascii').split('\t')  # gzip read bytes, hence the decoding
        try:
            dod[t[1]].update({t[0]:int(t[2])})
        except KeyError:
            dod[t[1]] = {t[0]:int(t[2])}

    df = DataFrame.from_dict(dod, orient='index')  # build the matrix
    df.fillna(value=0., inplace=True)  # many NaN, replace with zeros
    return AnnData(np.array(df), {'obs_names': df.index}, {'var_names': df.columns}, dtype=dtype)