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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 initialize_kwds(relaxation_kwds, n_samples, n_components, intrinsic_dim):
 """ Initialize relaxation keywords. Parameters relaxation_kwds : dict weights : numpy array, the weights step_method : string { 'fixed', 'momentum' } which optimizers to use linesearch : bool whether to do linesearch in search for eta in optimization verbose : bool whether to print reports to I/O when doing relaxation niter : int number of iterations to run. niter_trace : int number of iterations to be traced. presave : bool whether to store precomputed keywords to files or not. sqrd : bool whether to use squared norm in loss function. Default : True alpha : float shrinkage rate for previous gradient. Default : 0 projected : bool whether or not to optimize via projected gradient descent on differences S lossf : string { 'epsilon', 'rloss' } which loss function to optimize. Default : 'rloss' if n == d, otherwise 'epsilon' subset : numpy array Subset to do relaxation on. sub_dir : string sub_dir used to store the outputs. backup_base_dir : string base directory used to store outputs Final path will be backup_base_dir/sub_dir saveiter : int save backup on every saveiter iterations printiter : int print report on every printiter iterations save_init : bool whether to save Y0 and L before running relaxation. """

    new_relaxation_kwds = {
        'weights': np.array([],dtype=np.float64),
        'step_method': 'fixed',
        'linesearch': True,
        'verbose': False,
        'niter': 2000,
        'niter_trace': 0,
        'presave': False,
        'sqrd': True,
        'alpha': 0,
        'projected': False,
        'lossf': 'epsilon' if n_components > intrinsic_dim else 'rloss',
        'subset': np.arange(n_samples),
        'sub_dir': current_time_str(),
        'backup_base_dir': default_basedir,
        'saveiter': 10,
        'printiter': 1,
        'save_init': False,
    }

    new_relaxation_kwds.update(relaxation_kwds)

    backup_dir = os.path.join(new_relaxation_kwds['backup_base_dir'], new_relaxation_kwds['sub_dir'])
    new_relaxation_kwds['backup_dir'] = backup_dir
    create_output_dir(backup_dir)

    new_relaxation_kwds = convert_to_int(new_relaxation_kwds)

    if new_relaxation_kwds['weights'].shape[0] != 0:
        weights = np.absolute(new_relaxation_kwds['weights']).astype(np.float64)
        new_relaxation_kwds['weights'] = weights / np.sum(weights)

    if new_relaxation_kwds['lossf'] == 'epsilon':
        new_relaxation_kwds.setdefault('eps_orth', 0.1)

    if n_components != intrinsic_dim and new_relaxation_kwds['lossf'] == 'rloss':
        raise ValueError('loss function rloss is for n_components equal intrinsic_dim')

    if n_components == intrinsic_dim and new_relaxation_kwds['lossf'] == 'epsilon':
        raise ValueError('loss function rloss is for n_components equal intrinsic_dim')

    if new_relaxation_kwds['projected'] and new_relaxation_kwds['subset'].shape[0] < n_samples:
        raise ValueError('Projection derivative not working for subset methods.')

    prefix = 'projected' if new_relaxation_kwds['projected'] else 'nonprojected'
    new_relaxation_kwds['lossf'] = '{}_{}'.format(prefix,new_relaxation_kwds['lossf'])
    step_method = new_relaxation_kwds['step_method']

    if new_relaxation_kwds['linesearch'] == True:
        new_relaxation_kwds.setdefault('linesearch_first', False)
        init_eta_max = 2**11 if new_relaxation_kwds['projected'] else 2**4
        new_relaxation_kwds.setdefault('eta_max',init_eta_max)
    else:
        new_relaxation_kwds.setdefault('eta', 1.0)

    if step_method == 'momentum':
        new_relaxation_kwds.setdefault('m', 0.05)

    return new_relaxation_kwds