code: def forward(self, cnn_features, labels, counting_preds, images_mask):
      if self.is_train:
          _, num_steps = labels.shape
      else:
          num_steps = 36
  
      batch_size, _, height, width = cnn_features.shape
      images_mask = images_mask[:, :, :: self.ratio, :: self.ratio]
  
      word_probs = paddle.zeros((batch_size, num_steps, self.word_num))
      word_alpha_sum = paddle.zeros((batch_size, 1, height, width))
  
      hidden = self.init_hidden(cnn_features, images_mask)
      counting_context_weighted = self.counting_context_weight(
          counting_preds
      )
      cnn_features_trans = self.encoder_feature_conv(cnn_features)
  
      position_embedding = PositionEmbeddingSine(256, normalize=True)
      pos = position_embedding(
          cnn_features_trans, images_mask[:, 0, :, :]
      )
  
      cnn_features_trans = cnn_features_trans + pos
  
      word = paddle.ones(
          [batch_size, 1], dtype="int64"
      )  # init word as sos
      word = word.squeeze(axis=1)
      for i in range(num_steps):
          word_embedding = self.embedding(word)
          _, hidden = self.word_input_gru(word_embedding, hidden)
          word_context_vec, _, word_alpha_sum = self.word_attention(
              cnn_features,
              cnn_features_trans,
              hidden,
              word_alpha_sum,
              images_mask,
          )
  
          current_state = self.word_state_weight(hidden)
          word_weighted_embedding = self.word_embedding_weight(
              word_embedding
          )
          word_context_weighted = self.word_context_weight(
              word_context_vec
          )
  
          if self.dropout_prob:
              word_out_state = self.dropout(
                  current_state
                  + word_weighted_embedding
                  + word_context_weighted
                  + counting_context_weighted
              )
          else:
              word_out_state = (
                  current_state
                  + word_weighted_embedding
                  + word_context_weighted
                  + counting_context_weighted
              )
  
          word_prob = self.word_convert(word_out_state)
          word_probs[:, i] = word_prob
  
          if self.is_train:
              word = labels[:, i]
          else:
              word = word_prob.argmax(1)
              word = paddle.multiply(
                  word, labels[:, i]
              )  # labels are oneslike tensor in infer/predict mode
  
      return word_probs
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle.nn
  import paddle
  import math
label_desc: Use image node_id 2 for calling the AttDecoder obj's underlying member method code with example usage: obj.forward(cnn_features, labels, counting_preds, images_mask) and returns: word_probs