code: def forward(self, inputs):
      x = inputs["image"]
  
      out = self.conv0(x)
      out = self.downsample0(out)
      blocks = []
      for i, conv_block_i in enumerate(self.darknet_conv_block_list):
          out = conv_block_i(out)
          if i == self.freeze_at:
              out.stop_gradient = True
          if i in self.return_idx:
              blocks.append(out)
          if i < self.num_stages - 1:
              out = self.downsample_list[i](out)
      return blocks
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register, serializable
  from ppdet.modeling.ops import batch_norm, mish
  from ..shape_spec import ShapeSpec
label_desc: Use image node_id 2 for calling the DarkNet obj's underlying member method code with example usage: obj.forward(inputs) and returns: blocks