class_docstr: Vision Transformer with support for patch input
  
  This module is different from ppdet's VisionTransformer (from ppdet/modeling/backbones/visio_transformer.py),
  the main differences are:
  1.the module PatchEmbed.proj has padding set,padding=(4 + 2 * (ratio // 2 - 1), 4 + 2 * (ratio // 2 - 1),
    VisionTransformer dose not
  2.Attention module qkv is standard.but VisionTransformer provide more options
  3.MLP module only one Dropout,and VisionTransformer twice;
  4.VisionTransformer provide fpn layer,but the module does not.
code: def _init_weights(self):
      pretrained = self.pretrained
  
      if pretrained:
          if "http" in pretrained:  # URL
              path = paddle.utils.download.get_weights_path_from_url(
                  pretrained
              )
          else:  # model in local path
              path = pretrained
  
          load_state_dict = paddle.load(path)
          self.set_state_dict(load_state_dict)
          print("Load load_state_dict:", path)
  
imports: from collections.abc import Callable
  import numpy
  import paddle
  import paddle.nn
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal
  from ppdet.core.workspace import register, serializable
label_desc: Use image node_id 2 for calling the ViT obj's underlying member method code with example usage: obj._init_weights() without return types