Predict a sentence using BERTBiLSTMAttnNCRF without passing a dataloader

I had an issue while building a function that only predicts a sentence without passing a dataloader instance
Theses are the steps I followed:
sentence= 'put a sentence'
bert_tokens = []
tok_map = []
tokenizer = BertTokenizer.from_pretrained("bert-base-multilingual-cased")
# label2idx = {"[PAD]": pad_idx, '[CLS]': 1, '[SEP]': 2, "X": 3}
# idx2label = ["[PAD]", '[CLS]', '[SEP]', "X"]
orig_tokens = sentence.split()
orig_tokens = ["[CLS]"] + orig_tokens + ["[SEP]"]
for origin_token in orig_tokens:
cur_tokens = tokenizer.tokenize(origin_token)
bert_tokens.extend(cur_tokens)
tok_map.append(len(bert_tokens))
input_ids = tokenizer.convert_tokens_to_ids(bert_tokens)
input_mask = [1] * len(input_ids)
while len(input_ids) < 424:
input_mask.append(0)
tok_map.append(-1)
input_ids.append(0)
input_type_ids = [0] * len(input_ids)

The problem is I couldn't figure out what batch is in order to predict using model.forward(batch)
I tried this:

batch=[[0],[0],[0]]
batch[0]=input_ids
batch[1]=input_type_ids
batch[2]=input_mask
learner.model.forward(batch)
and this is what I got:

~/ner-bert-master-last-version/ner-bert-master-last-version/modules/models/bert_models.py in forward(self, batch)
46 def forward(self, batch):
47 input_, labels_mask, input_type_ids = batch[:3]
---> 48 input_embeddings = self.embeddings(batch)
49 output, _ = self.lstm.forward(batch)
50 output, _ = self.attn(output, output, output, None)

~/.local/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),

~/ner-bert-master-last-version/ner-bert-master-last-version/modules/layers/embedders.py in forward(self, batch)
59 token_type_ids=batch[2],
60 attention_mask=batch[1],
---> 61 output_all_encoded_layers=self.config["mode"] == "weighted")
62 if self.config["mode"] == "weighted":
63 encoded_layers = torch.stack([a * b for a, b in zip(encoded_layers, self.bert_weights)])

~/.local/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),

~/.local/lib/python3.7/site-packages/pytorch_pretrained_bert/modeling.py in forward(self, input_ids, token_type_ids, attention_mask, output_all_encoded_layers)
718 # this attention mask is more simple than the triangular masking of causal attention
719 # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
--> 720 extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
721
722 # Since attention_mask is 1.0 for positions we want to attend and 0.0 for

AttributeError: 'list' object has no attribute 'unsqueeze'

can you please help!