code: def forward_test(self, feat, out_enc, valid_ratio):
      src_mask = self._get_mask(out_enc, valid_ratio)
      N = out_enc.shape[0]
      init_target_seq = paddle.full(
          (N, self.max_seq_len + 1), self.padding_idx, dtype="int64"
      )
      # bsz * seq_len
      init_target_seq[:, 0] = self.start_idx
  
      outputs = []
      for step in range(0, paddle.to_tensor(self.max_seq_len)):
          decoder_output = self._attention(
              init_target_seq, out_enc, src_mask=src_mask
          )
          # bsz * seq_len * C
          step_result = F.softmax(
              self.classifier(decoder_output[:, step, :]), axis=-1
          )
          # bsz * num_classes
          outputs.append(step_result)
          step_max_index = paddle.argmax(step_result, axis=-1)
          init_target_seq[:, step + 1] = step_max_index
  
      outputs = paddle.stack(outputs, axis=1)
  
      return outputs
  
imports: import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr, reshape, transpose
  from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
  from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
  from paddle.nn.initializer import KaimingNormal, Uniform, Constant
label_desc: Use image node_id 7 for calling the SATRNDecoder obj's underlying member method code with example usage: obj.forward_test(feat, out_enc, valid_ratio) and returns: outputs