<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def load_model(itos_filename, classifier_filename, num_classes):
 """Load the classifier and int to string mapping Args: itos_filename (str):
 The filename of the int to string mapping file (usually called itos.pkl) classifier_filename (str):
 The filename of the trained classifier Returns: string to int mapping, trained classifer model """


    # load the int to string mapping file
    itos = pickle.load(Path(itos_filename).open('rb'))
    # turn it into a string to int mapping (which is what we need)
    stoi = collections.defaultdict(lambda:0, {str(v):int(k) for k,v in enumerate(itos)})

    # these parameters aren't used, but this is the easiest way to get a model
    bptt,em_sz,nh,nl = 70,400,1150,3
    dps = np.array([0.4,0.5,0.05,0.3,0.4])*0.5
    vs = len(itos)

    model = get_rnn_classifer(bptt, 20*70, num_classes, vs, emb_sz=em_sz, n_hid=nh, n_layers=nl, pad_token=1,
            layers=[em_sz*3, 50, num_classes], drops=[dps[4], 0.1],
            dropouti=dps[0], wdrop=dps[1], dropoute=dps[2], dropouth=dps[3])

    # load the trained classifier
    model.load_state_dict(torch.load(classifier_filename, map_location=lambda storage, loc: storage))

    # put the classifier into evaluation mode
    model.reset()
    model.eval()

    return stoi, model