docstr: model inference
  batch_size: batch size
  *input_datas: x1, x2, ..., xn
docstr_tok: ["model","inference","batch_size",":","batch","size","*","input_datas",":","x1",",","x2",",","...",",","xn"]
code: def forward(self, *input_datas, batch_size=1):
      """
      model inference
      batch_size: batch size
      *input_datas: x1, x2, ..., xn
      """
      # 切分输入数据
      datas_num = input_datas[0].shape[0]
      split_num = (
          datas_num // batch_size + 1
          if datas_num % batch_size != 0
          else datas_num // batch_size
      )
      input_datas = [
          np.array_split(input_data, split_num)
          for input_data in input_datas
      ]
  
      # 遍历输入数据进行预测
      outputs = {}
      for step in range(split_num):
          for i in range(self.input_num):
              input_data = input_datas[i][step].copy()
              self.input_handles[i].copy_from_cpu(input_data)
  
          self.predictor.run()
  
          for i in range(self.output_num):
              output = self.output_handles[i].copy_to_cpu()
              if i in outputs:
                  outputs[i].append(output)
              else:
                  outputs[i] = [output]
  
      # 预测结果合并
      for key in outputs.keys():
          outputs[key] = np.concatenate(outputs[key], 0)
  
      outputs = [v for v in outputs.values()]
  
      # 返回预测结果
      return tuple(outputs) if len(outputs) > 1 else outputs[0]
  
file_path: PaddleHub/modules/image/Image_gan/style_transfer/animegan_v2_shinkai_53/model.py
imports: import os
  import numpy
  from paddle.inference import Config
  from paddle.inference import create_predictor
label_desc: Use image node_id 6 for calling the InferenceModel obj's underlying member method code with example usage: obj.forward() and returns: unknown