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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 get_language_model(n_tok, emb_sz, n_hid, n_layers, pad_token, dropout=0.4, dropouth=0.3, dropouti=0.5, dropoute=0.1, wdrop=0.5, tie_weights=True, qrnn=False, bias=False):
 """Returns a SequentialRNN model. A RNN_Encoder layer is instantiated using the parameters provided. This is followed by the creation of a LinearDecoder layer. Also by default (i.e. tie_weights = True), the embedding matrix used in the RNN_Encoder is used to instantiate the weights for the LinearDecoder layer. The SequentialRNN layer is the native torch's Sequential wrapper that puts the RNN_Encoder and LinearDecoder layers sequentially in the model. Args: n_tok (int):
 number of unique vocabulary words (or tokens) in the source dataset emb_sz (int):
 the embedding size to use to encode each token n_hid (int):
 number of hidden activation per LSTM layer n_layers (int):
 number of LSTM layers to use in the architecture pad_token (int):
 the int value used for padding text. dropouth (float):
 dropout to apply to the activations going from one LSTM layer to another dropouti (float):
 dropout to apply to the input layer. dropoute (float):
 dropout to apply to the embedding layer. wdrop (float):
 dropout used for a LSTM's internal (or hidden) recurrent weights. tie_weights (bool):
 decide if the weights of the embedding matrix in the RNN encoder should be tied to the weights of the LinearDecoder layer. qrnn (bool):
 decide if the model is composed of LSTMS (False) or QRNNs (True). bias (bool):
 decide if the decoder should have a bias layer or not. Returns: A SequentialRNN model """

    rnn_enc = RNN_Encoder(n_tok, emb_sz, n_hid=n_hid, n_layers=n_layers, pad_token=pad_token,
                 dropouth=dropouth, dropouti=dropouti, dropoute=dropoute, wdrop=wdrop, qrnn=qrnn)
    enc = rnn_enc.encoder if tie_weights else None
    return SequentialRNN(rnn_enc, LinearDecoder(n_tok, emb_sz, dropout, tie_encoder=enc, bias=bias))