<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def train_supervised( input, lr=0.1, dim=100, ws=5, epoch=5, minCount=1, minCountLabel=0, minn=0, maxn=0, neg=5, wordNgrams=1, loss="softmax", bucket=2000000, thread=multiprocessing.cpu_count() - 1, lrUpdateRate=100, t=1e-4, label="__label__", verbose=2, pretrainedVectors="", ):
 """ Train a supervised model and return a model object. input must be a filepath. The input text does not need to be tokenized as per the tokenize function, but it must be preprocessed and encoded as UTF-8. You might want to consult standard preprocessing scripts such as tokenizer.perl mentioned here: http://www.statmt.org/wmt07/baseline.html The input file must must contain at least one label per line. For an example consult the example datasets which are part of the fastText repository such as the dataset pulled by classification-example.sh. """

    model = "supervised"
    a = _build_args(locals())
    ft = _FastText()
    fasttext.train(ft.f, a)
    return ft