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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 best_kmers(dt, response, sequence, k=6, consider_shift=True, n_cores=1, seq_align="start", trim_seq_len=None):
 """ Find best k-mers for CONCISE initialization. Args: dt (pd.DataFrame):
 Table containing response variable and sequence. response (str):
 Name of the column used as the reponse variable. sequence (str):
 Name of the column storing the DNA/RNA sequences. k (int):
 Desired k-mer length. n_cores (int):
 Number of cores to use for computation. It can use up to 3 cores. consider_shift (boolean):
 When performing stepwise k-mer selection. Is TATTTA similar to ATTTAG? seq_align (str):
 one of ``{"start", "end"}``. To which end should we align sequences? trim_seq_len (int):
 Consider only first `trim_seq_len` bases of each sequence when generating the sequence design matrix. If :python:`None`, set :py:attr:`trim_seq_len` to the longest sequence length, hence whole sequences are considered. Returns: string list: Best set of motifs for this dataset sorted with respect to confidence (best candidate occuring first). Details: First a lasso model gets fitted to get a set of initial motifs. Next, the best subset of unrelated motifs is selected by stepwise selection. """

    y = dt[response]
    seq = dt[sequence]

    if trim_seq_len is not None:
        seq = pad_sequences(seq, align=seq_align, maxlen=trim_seq_len)
        seq = [s.replace("N", "") for s in seq]

    dt_kmer = kmer_count(seq, k)
    Xsp = csc_matrix(dt_kmer)
    en = ElasticNet(alpha=1, standardize=False, n_splits=3)
    en.fit(Xsp, y)
    # which coefficients are nonzero?=
    nonzero_kmers = dt_kmer.columns.values[en.coef_ != 0].tolist()

    # perform stepwise selection
    #
    # TODO - how do we deal with the intercept?

    # largest number of motifs where they don't differ by more than 1 k-mer
    def find_next_best(dt_kmer, y, selected_kmers, to_be_selected_kmers, consider_shift=True):
        """
        perform stepwise model selection while preventing to add a motif similar to the
        already selected motifs.
        """
        F, pval = f_regression(dt_kmer[to_be_selected_kmers], y)
        kmer = to_be_selected_kmers.pop(pval.argmin())
        selected_kmers.append(kmer)

        def select_criterion(s1, s2, consider_shift=True):
            if hamming_distance(s1, s2) <= 1:
                return False
            if consider_shift and hamming_distance(s1[1:], s2[:-1]) == 0:
                return False
            if consider_shift and hamming_distance(s1[:-1], s2[1:]) == 0:
                return False
            return True

        to_be_selected_kmers = [ckmer for ckmer in to_be_selected_kmers
                                if select_criterion(ckmer, kmer, consider_shift)]
        if len(to_be_selected_kmers) == 0:
            return selected_kmers
        else:
            # regress out the new feature
            lm = LinearRegression()
            lm.fit(dt_kmer[selected_kmers], y)
            y_new = y - lm.predict(dt_kmer[selected_kmers])
            return find_next_best(dt_kmer, y_new, selected_kmers, to_be_selected_kmers, consider_shift)

    selected_kmers = find_next_best(dt_kmer, y, [], nonzero_kmers, consider_shift)

    return selected_kmers