code: def transpose_weights(m_type, w_dict, k, v):
      if m_type == "conv":
          if len(v.shape) > 1:
              # conv weights
              v = np.transpose(v.numpy(), (2, 3, 1, 0)).astype(
                  np.float32
              )
          w_dict[k] = v
      elif m_type == "dwconv":
          if len(v.shape) > 1:
              # dwconv weights
              v = np.transpose(v.numpy(), (2, 3, 0, 1)).astype(
                  np.float32
              )
          w_dict[k] = v
      elif m_type == "linear":
          if len(v.shape) > 1:
              v = np.transpose(v.numpy(), (1, 0)).astype(np.float32)
          w_dict[k] = v
      elif m_type == "norm":
          w_dict[k] = v
      else:
          ValueError(f"not support type:{m_type}")
  
label_desc: Use image node_id 1 for calling a global function with example usage: transpose_weights(m_type, w_dict, k, v) without return types