想请教各位一个问题！！！！！想知道这是为什么，看官方api文档说summary()中的参数是可选的，可是我在学习时想使用summary()打印网络的基础结构和参数信息看看，报如下错误：

---------------------------------------------------------------------------ValueError                                Traceback (most recent call last) in 
----> 1 model_cnn.summary()
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/hapi/model.py in summary(self, input_size, dtype)
   1879         else:
   1880             _input_size = self._inputs
-> 1881         return summary(self.network, _input_size, dtype)
   1882 
   1883     def _verify_spec(self, specs, shapes=None, dtypes=None, is_input=False):
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/hapi/model_summary.py in summary(net, input_size, dtypes)
    147 
    148     _input_size = _check_input(_input_size)
--> 149     result, params_info = summary_string(net, _input_size, dtypes)
    150     print(result)
    151 
 in summary_string(model, input_size, dtypes)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/base.py in _decorate_function(func, *args, **kwargs)
    313         def _decorate_function(func, *args, **kwargs):
    314             with self:
--> 315                 return func(*args, **kwargs)
    316 
    317         @decorator.decorator
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/hapi/model_summary.py in summary_string(model, input_size, dtypes)
    274 
    275     # make a forward pass
--> 276     model(*x)
    277 
    278     # remove these hooks
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py in __call__(self, *inputs, **kwargs)
    900                 self._built = True
    901 
--> 902             outputs = self.forward(*inputs, **kwargs)
    903 
    904             for forward_post_hook in self._forward_post_hooks.values():
 in forward(self, text, seq_len)
     32     def forward(self, text, seq_len=None):
     33         # Shape: (batch_size, num_tokens, embedding_dim)
---> 34         embedded_text = self.embedder(text)
     35         print('after word-embeding:', embedded_text.shape)
     36         # Shape: (batch_size, len(ngram_filter_sizes)*num_filter)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py in __call__(self, *inputs, **kwargs)
    900                 self._built = True
    901 
--> 902             outputs = self.forward(*inputs, **kwargs)
    903 
    904             for forward_post_hook in self._forward_post_hooks.values():
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/common.py in forward(self, x)
   1361             padding_idx=self._padding_idx,
   1362             sparse=self._sparse,
-> 1363             name=self._name)
   1364 
   1365     def extra_repr(self):
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/functional/input.py in embedding(x, weight, padding_idx, sparse, name)
    198         return core.ops.lookup_table_v2(
    199             weight, x, 'is_sparse', sparse, 'is_distributed', False,
--> 200             'remote_prefetch', False, 'padding_idx', padding_idx)
    201     else:
    202         helper = LayerHelper('embedding', **locals())
ValueError: (InvalidArgument) Tensor holds the wrong type, it holds float, but desires to be int64_t.
  [Hint: Expected valid == true, but received valid:0 != true:1.] (at /paddle/paddle/fluid/framework/tensor_impl.h:33)
  [operator < lookup_table_v2 > error]

源码如下：

class CNNModel(nn.Layer):

    def __init__(self,
                 vocab_size,
                 num_classes,
                 emb_dim=128,
                 padding_idx=0,
                 num_filter=128,
                 ngram_filter_sizes=(3, ),
                 fc_hidden_size=96):
        super().__init__()
        self.embedder = nn.Embedding(
            vocab_size, emb_dim, padding_idx=padding_idx)
        self.encoder = ppnlp.seq2vec.CNNEncoder(
            emb_dim=emb_dim,
            num_filter=num_filter,
            ngram_filter_sizes=ngram_filter_sizes)
        self.fc = nn.Linear(self.encoder.get_output_dim(), fc_hidden_size)
        self.output_layer = nn.Linear(fc_hidden_size, num_classes)

    def forward(self, text, seq_len=None):
        # Shape: (batch_size, num_tokens, embedding_dim)
        embedded_text = self.embedder(text)
        print('after word-embeding:', embedded_text.shape)
        # Shape: (batch_size, len(ngram_filter_sizes)*num_filter)
        encoder_out = self.encoder(embedded_text)
        encoder_out = paddle.tanh(encoder_out)
        # Shape: (batch_size, fc_hidden_size)
        fc_out = self.fc(encoder_out)
        # Shape: (batch_size, num_classes)
        logits = self.output_layer(fc_out)
        return logits

model_cnn = CNNModel(27665,2)
model_cnn = paddle.Model(model_cnn)
model_cnn.summary()