code: def __init__(
      self,
      in_channels=768,
      out_channels=17,
      num_deconv_layers=3,
      num_deconv_filters=(256, 256, 256),
      num_deconv_kernels=(4, 4, 4),
      extra=None,
      in_index=0,
      input_transform=None,
      align_corners=False,
      upsample=0,
      flip_pairs=None,
      shift_heatmap=False,
      target_type="GaussianHeatmap",
  ):
      super(TopdownHeatmapSimpleHead, self).__init__()
  
      self.in_channels = in_channels
      self.upsample = upsample
      self.flip_pairs = flip_pairs
      self.shift_heatmap = shift_heatmap
      self.target_type = target_type
  
      self._init_inputs(in_channels, in_index, input_transform)
      self.in_index = in_index
      self.align_corners = align_corners
  
      if extra is not None and not isinstance(extra, dict):
          raise TypeError("extra should be dict or None.")
  
      if num_deconv_layers > 0:
          self.deconv_layers = self._make_deconv_layer(
              num_deconv_layers,
              num_deconv_filters,
              num_deconv_kernels,
          )
      elif num_deconv_layers == 0:
          self.deconv_layers = nn.Identity()
      else:
          raise ValueError(
              f"num_deconv_layers ({num_deconv_layers}) should >= 0."
          )
  
      identity_final_layer = False
      if extra is not None and "final_conv_kernel" in extra:
          assert extra["final_conv_kernel"] in [0, 1, 3]
          if extra["final_conv_kernel"] == 3:
              padding = 1
          elif extra["final_conv_kernel"] == 1:
              padding = 0
          else:
              # 0 for Identity mapping.
              identity_final_layer = True
          kernel_size = extra["final_conv_kernel"]
      else:
          kernel_size = 1
          padding = 0
  
      if identity_final_layer:
          self.final_layer = nn.Identity()
      else:
          conv_channels = (
              num_deconv_filters[-1]
              if num_deconv_layers > 0
              else self.in_channels
          )
  
          layers = []
          if extra is not None:
              num_conv_layers = extra.get("num_conv_layers", 0)
              num_conv_kernels = extra.get(
                  "num_conv_kernels", [1] * num_conv_layers
              )
  
              for i in range(num_conv_layers):
                  layers.append(
                      nn.Conv2D(
                          in_channels=conv_channels,
                          out_channels=conv_channels,
                          kernel_size=num_conv_kernels[i],
                          stride=1,
                          padding=(num_conv_kernels[i] - 1) // 2,
                      )
                  )
                  layers.append(nn.BatchNorm2D(conv_channels))
                  layers.append(nn.ReLU())
  
          layers.append(
              nn.Conv2D(
                  in_channels=conv_channels,
                  out_channels=out_channels,
                  kernel_size=kernel_size,
                  stride=1,
                  padding=(padding, padding),
              )
          )
  
          if len(layers) > 1:
              self.final_layer = nn.Sequential(*layers)
          else:
              self.final_layer = layers[0]
  
      self.init_weights()
  
file_path: PaddleDetection/ppdet/modeling/heads/vitpose_head.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from ppdet.modeling.keypoint_utils import resize, flip_back
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal
  from ppdet.modeling.layers import ConvTranspose2d, BatchNorm2d
label_desc: Use image node_id 1 to create a new TopdownHeatmapSimpleHead object from inherited base classes: nn with example: obj = TopdownHeatmapSimpleHead(in_channels, out_channels, num_deconv_layers, num_deconv_filters, num_deconv_kernels, extra, in_index, input_transform, align_corners, upsample, flip_pairs, shift_heatmap, target_type)