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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 prepare_data(dt, features, response, sequence, id_column=None, seq_align="end", trim_seq_len=None):
 """ Prepare data for Concise.train or ConciseCV.train. Args: dt: A pandas DataFrame containing all the required data. features (List of strings):
 Column names of `dt` used to produce the features design matrix. These columns should be numeric. response (str or list of strings):
 Name(s) of column(s) used as a reponse variable. sequence (str):
 Name of the column storing the DNA/RNA sequences. id_column (str):
 Name of the column used as the row identifier. 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. standardize_features (bool):
 If True, column in the returned matrix matrix :py:attr:`X_seq` are normalied to have zero mean and unit variance. Returns: tuple: Tuple with elements: :code:`(X_feat: X_seq, y, id_vec)`, where: - :py:attr:`X_feat`: features design matrix of shape :code:`(N, D)`, where N is :code:`len(dt)` and :code:`D = len(features)` - :py:attr:`X_seq`: sequence matrix of shape :code:`(N, 1, trim_seq_len, 4)`. It represents 1-hot encoding of the DNA/RNA sequence. - :py:attr:`y`: Response variable 1-column matrix of shape :code:`(N, 1)` - :py:attr:`id_vec`: 1D Character array of shape :code:`(N)`. It represents the ID's of individual rows. Note: One-hot encoding of the DNA/RNA sequence is the following: .. code:: python { "A": np.array([1, 0, 0, 0]), "C": np.array([0, 1, 0, 0]), "G": np.array([0, 0, 1, 0]), "T": np.array([0, 0, 0, 1]), "U": np.array([0, 0, 0, 1]), "N": np.array([0, 0, 0, 0]), } """

    if type(response) is str:
        response = [response]

    X_feat = np.array(dt[features], dtype="float32")
    y = np.array(dt[response], dtype="float32")
    X_seq = encodeDNA(seq_vec=dt[sequence],
                      maxlen=trim_seq_len,
                      seq_align=seq_align)
    X_seq = np.array(X_seq, dtype="float32")
    id_vec = np.array(dt[id_column])

    return X_feat, X_seq, y, id_vec