class_docstr: Vision Transformer with support for patch input
      
class_docstr_tok: ["Vision","Transformer","with","support","for","patch","input"]
code: def init_fpn(self, embed_dim=768, patch_size=16, out_with_norm=False):
      if patch_size == 16:
          self.fpn1 = nn.Sequential(
              nn.Conv2DTranspose(
                  embed_dim, embed_dim, kernel_size=2, stride=2
              ),
              nn.BatchNorm2D(embed_dim),
              nn.GELU(),
              nn.Conv2DTranspose(
                  embed_dim, embed_dim, kernel_size=2, stride=2
              ),
          )
  
          self.fpn2 = nn.Sequential(
              nn.Conv2DTranspose(
                  embed_dim, embed_dim, kernel_size=2, stride=2
              ),
          )
  
          self.fpn3 = Identity()
  
          self.fpn4 = nn.MaxPool2D(kernel_size=2, stride=2)
      elif patch_size == 8:
          self.fpn1 = nn.Sequential(
              nn.Conv2DTranspose(
                  embed_dim, embed_dim, kernel_size=2, stride=2
              ),
          )
  
          self.fpn2 = Identity()
  
          self.fpn3 = nn.Sequential(
              nn.MaxPool2D(kernel_size=2, stride=2),
          )
  
          self.fpn4 = nn.Sequential(
              nn.MaxPool2D(kernel_size=4, stride=4),
          )
  
      if not out_with_norm:
          self.norm = Identity()
      else:
          self.norm = nn.LayerNorm(embed_dim, epsilon=self.epsilon)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  import math
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import Constant, TruncatedNormal
  from ppdet.modeling.shape_spec import ShapeSpec
  from ppdet.core.workspace import register, serializable
  from .transformer_utils import zeros_, DropPath, Identity, window_partition, window_unpartition
  from ..initializer import linear_init_
label_desc: Use image node_id 4 for calling the VisionTransformer2D obj's underlying member method code with example usage: obj.init_fpn(embed_dim, patch_size, out_with_norm) without return types