class_docstr: As the joint influence from external environment and internal cognition, different kinds of user interests are
  evolving over time. Interest Evolving Layer can capture interest evolving process that is relative to the target
  item.
code: def __init__(
      self,
      mask_mat,
      input_size,
      rnn_hidden_size,
      att_hidden_size=(80, 40),
      activation="sigmoid",
      softmax_stag=True,
      gru="GRU",
  ):
      super(InterestEvolvingLayer, self).__init__()
  
      self.mask_mat = mask_mat
      self.gru = gru
  
      if gru == "GRU":
          self.attention_layer = SequenceAttLayer(
              mask_mat, att_hidden_size, activation, softmax_stag, False
          )
          self.dynamic_rnn = nn.GRU(
              input_size=input_size,
              hidden_size=rnn_hidden_size,
              batch_first=True,
          )
  
      elif gru == "AIGRU":
          self.attention_layer = SequenceAttLayer(
              mask_mat, att_hidden_size, activation, softmax_stag, True
          )
          self.dynamic_rnn = nn.GRU(
              input_size=input_size,
              hidden_size=rnn_hidden_size,
              batch_first=True,
          )
  
      elif gru == "AGRU" or gru == "AUGRU":
          self.attention_layer = SequenceAttLayer(
              mask_mat, att_hidden_size, activation, softmax_stag, True
          )
          self.dynamic_rnn = DynamicRNN(
              input_size=input_size,
              hidden_size=rnn_hidden_size,
              gru=gru,
          )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.init import xavier_normal_, constant_
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence, PackedSequence
  from recbole.utils import ModelType, InputType, FeatureType
  from recbole.model.layers import FMEmbedding, MLPLayers, ContextSeqEmbLayer, SequenceAttLayer
  from recbole.model.abstract_recommender import SequentialRecommender
label_desc: Use image node_id 1 to create a new InterestEvolvingLayer object from inherited base classes: nn with example: obj = InterestEvolvingLayer(mask_mat, input_size, rnn_hidden_size, att_hidden_size, activation, softmax_stag, gru)