Whether the model can include the Embedding layer?

[xiuzhilu]

My model is defined as follows：
def create_model():
#input
input_layer=Input(shape=(max_squence_len,),name="x_seq")
embedding_layer=Embedding(input_dim=max_token_num,output_dim=embedding_dim) (input_layer)
#conv layers
convs=[]
filter_sizes=[2,3,4]
for fs in filter_sizes:
l_conv=Conv1D(filters=50,kernel_size=fs,activation="relu")(embedding_layer)
l_pool=MaxPooling1D(pool_size=2)(l_conv)
l_pool=Flatten()(l_pool)
convs.append(l_pool)
merge=concatenate(convs,axis=1)
out=Dropout(0.5)(merge)
output=Dense(32, activation='relu')(out)
output=Dense(units=31, activation='softmax')(output)
model=Model([input_layer],output)
adam = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss="categorical_crossentropy",optimizer=adam,metrics=['accuracy'])
model=KerasClassifier(build_fn=self.create_model,batch_size=64,epochs=25)
batch_size = [10, 20, 40, 60, 80, 100]
epochs = [5,10,15,20,25,30]
param_grid = dict(batch_size=batch_size, epochs=epochs)
grid = GridSearchCV(estimator=model, param_grid=param_grid,cv=5)
grid.fit(x_train,y_train)
print("best_score:",grid.best_score_,"best_param:",grid.best_params_)
But the program throws an error:TypeError: cannot deepcopy this pattern object
can you help me solve this problem?