class_docstr: NAML model(Neural News Recommendation with Attentive Multi-View Learning)
  
  Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang and Xing Xie,
  Neural News Recommendation with Attentive Multi-View Learning, IJCAI 2019
  
  Attributes:
      word2vec_embedding (numpy.ndarray): Pretrained word embedding matrix.
      hparam (object): Global hyper-parameters.
class_docstr_tok: ["NAML","model","(","Neural","News","Recommendation","with","Attentive","Multi-View","Learning",")","Chuhan","Wu",",","Fangzhao","Wu",",","Mingxiao","An",",","Jianqiang","Huang",",","Yongfeng","Huang","and","Xing","Xie",",","Neural","News","Recommendation","with","Attentive","Multi-View","Learning",",","IJCAI","2019","Attributes",":","word2vec_embedding","(","numpy.ndarray",")",":","Pretrained","word","embedding","matrix",".","hparam","(","object",")",":","Global","hyper-parameters","."]
code: def _get_input_label_from_iter(self, batch_data):
      input_feat = [
          batch_data["clicked_title_batch"],
          batch_data["clicked_ab_batch"],
          batch_data["clicked_vert_batch"],
          batch_data["clicked_subvert_batch"],
          batch_data["candidate_title_batch"],
          batch_data["candidate_ab_batch"],
          batch_data["candidate_vert_batch"],
          batch_data["candidate_subvert_batch"],
      ]
      input_label = batch_data["labels"]
      return input_feat, input_label
  
file_path: recommenders/recommenders/models/newsrec/models/naml.py
imports: import numpy
  import tensorflow.keras
  from tensorflow.keras import layers
  from recommenders.models.newsrec.models.base_model import BaseModel
  from recommenders.models.newsrec.models.layers import AttLayer2
label_desc: Use image node_id 2 for calling the NAMLModel obj's underlying member method code with example usage: obj._get_input_label_from_iter(batch_data) and returns: input_feat,  input_label