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def load_base_model(model_path, epoch, ctx, layer_name=None, n_inputs=2):
    """ Loads the model from given model path 
        and returns a subnetwork that gives output from layer_name 
    """
    net = gluon.nn.SymbolBlock.imports(
        model_path + "-symbol.json",
        ['data%i' % i for i in range(n_inputs)],
        model_path + "-%.4d.params" % epoch,
        ctx=ctx,
    )

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few_words = ['great', 'excellent', 'best', 'perfect', 'wonderful', 'well',
       'fun', 'love', 'amazing', 'also', 'enjoyed', 'favorite', 'it',
       'and', 'loved', 'highly', 'bit', 'job', 'today', 'beautiful',
       'you', 'definitely', 'superb', 'brilliant', 'world', 'liked',
       'still', 'enjoy', 'life', 'very', 'especially', 'see', 'fantastic',
       'both', 'shows', 'good', 'may', 'terrific', 'heart', 'classic',
       'will', 'enjoyable', 'beautifully', 'always', 'true', 'perfectly',
       'surprised', 'think', 'outstanding', 'most',
             
       'bad', 'worst', 'awful', 'waste', 'boring', 'poor', 'terrible',

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def train(network, 
          train_data, 
          holdout_data, 
          loss,
          epochs, 
          ctx, 
          lr=1e-2,
          wd=1e-5,
          optimizer='adam'):
    

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# Preparation of network arguments
ctx = [mx.gpu(0)]  # use a GPU
tt = transformer_pipe.named_steps['token2index'] # to get token to integer map
max_idx = max(tt.tok2idx.values())+1  # size of vocabulary of all tokens in training data
tok_embed_dim = 64 # embedding size of each token
review_embed_dim = 50  # embedding size of hidden state in GRU

input_output_embed_map = {"token_embed": (max_idx, tok_embed_dim),
                         "hidden_embed": (None, review_embed_dim)}
                         

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class CustomSeqNet(gluon.nn.HybridBlock):
    """
    Custom defined network for sequence data that is used to predict a binary label.   
    
    """
    def __init__(self, input_output_embed_map, dense_sizes=[100], dropouts=[0.2], activation="relu"):
        """
        input_output_embed_map: {"token_embed": (max_tok_idx, tok_embed_dim), "hidden_embed": (,hidden_embed_dim))}
        """
        self.dense_sizes = dense_sizes  # list of output dimension of dense layers

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def get_dataloader(dataset,
                   dataset_type="train",  # valid/test
                   batch_size=256,
                   bucket_num=5,
                   shuffle=True,  # true for training
                   num_workers=1):
    
    # Batchify function appends the length of each sequence to feed as addtional input 
    combined_batchify_fn = nlp.data.batchify.Tuple(
        nlp.data.batchify.Pad(axis=0, ret_length=True),

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class Tokenize(BaseEstimator, TransformerMixin):
    """
    Takes in pandas series and applies tokenization on each row based on given split pattern. 
    """
    def __init__(self, split_pat=f"([{string.punctuation}])"):
        self.split_pat = split_pat  # re pattern used to split string to tokens. default splits over any string punctuation
    
    def tokenize(self, s):
        """ Tokenize string """
        re_tok = re.compile(self.split_pat)

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# Transformers present in Pipeline work in series. 1->2->3->4
transformer_pipe = Pipeline(steps=[
("null_impute", NullImputer(strategy="constant", fill_value="null")),
("lower_case", LowerCaser()),
("tokenize", Tokenize(f"([{string.punctuation}])")),
("token2index", Tok2Idx())
])

X_train_transformed = transformer_pipe.fit_transform(X_train) # fit and transform on train data
X_valid_transformed = transformer_pipe.transform(X_valid)

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