I dont see anywhere 'mask_feat_list' argument in embedding_lookup function.

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Describe the question(问题描述)
I get an error I think because of this. This is the full track of error.

dnn_input_emb_list = embedding_lookup(X, model.embedding_dict, model.feature_index, model.sparse_feature_columns,
                                      mask_feat_list= model.item_features, to_list=True)
Traceback (most recent call last):

  File "<ipython-input-201-f7445a26a1d5>", line 2, in <module>
    mask_feat_list= model.item_features, to_list=True)

  File "/home/hojun/anaconda3/envs/ai/lib/python3.6/site-packages/deepctr_torch/inputs.py", line 206, in embedding_lookup
    emb = sparse_embedding_dict[embedding_name](input_tensor)

  File "/home/hojun/anaconda3/envs/ai/lib/python3.6/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)

  File "/home/hojun/anaconda3/envs/ai/lib/python3.6/site-packages/torch/nn/modules/sparse.py", line 126, in forward
    self.norm_type, self.scale_grad_by_freq, self.sparse)

  File "/home/hojun/anaconda3/envs/ai/lib/python3.6/site-packages/torch/nn/functional.py", line 1852, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)

IndexError: index out of range in self

As you see the function does not have mask_feat_list anywhere. Is it right that I get an error because of this?

def embedding_lookup(X, sparse_embedding_dict, sparse_input_dict, sparse_feature_columns, return_feat_list=(),
                     mask_feat_list=(), to_list=False):
    """
        Args:
            X: input Tensor [batch_size x hidden_dim]
            sparse_embedding_dict: nn.ModuleDict, {embedding_name: nn.Embedding}
            sparse_input_dict: OrderedDict, {feature_name:(start, start+dimension)}
            sparse_feature_columns: list, sparse features
            return_feat_list: list, names of feature to be returned, defualt () -> return all features
            mask_feat_list, list, names of feature to be masked in hash transform
        Return:
            group_embedding_dict: defaultdict(list)
    """
    group_embedding_dict = defaultdict(list)
    for fc in sparse_feature_columns:
        feature_name = fc.name
        embedding_name = fc.embedding_name
        if (len(return_feat_list) == 0 or feature_name in return_feat_list):
            # TODO: add hash function
            # if fc.use_hash:
            #     raise NotImplementedError("hash function is not implemented in this version!")
            lookup_idx = np.array(sparse_input_dict[feature_name])
            input_tensor = X[:, lookup_idx[0]:lookup_idx[1]].long()
            emb = sparse_embedding_dict[embedding_name](input_tensor)
            group_embedding_dict[fc.group_name].append(emb)
    if to_list:
        return list(chain.from_iterable(group_embedding_dict.values()))
    return group_embedding_dict

Start with the embedding_lookup function in deepctr_torch/inputs.py, comparing its mask_feat_list parameter and docstring with the reported call and traceback. Reproduce the IndexError with the shown inputs, then determine whether mask_feat_list is expected to affect lookup behavior or whether the invalid index has another cause. Done means the behavior is clarified or covered by an appropriate fix and test.