class_docstr: Vision Transformer with support for patch input
      
class_docstr_tok: ["Vision","Transformer","with","support","for","patch","input"]
code: def interpolate_pos_encoding(self, x, w, h):
      npatch = x.shape[1] - 1
      N = self.pos_embed.shape[1] - 1
      w0 = w // self.patch_embed.patch_size
      h0 = h // self.patch_embed.patch_size
      if (
          npatch == N
          and w0 == self.patch_embed.num_patches_w
          and h0 == self.patch_embed.num_patches_h
      ):
          return self.pos_embed
      class_pos_embed = self.pos_embed[:, 0]
      patch_pos_embed = self.pos_embed[:, 1:]
      dim = x.shape[-1]
      # we add a small number to avoid floating point error in the interpolation
      # see discussion at https://github.com/facebookresearch/dino/issues/8
      # w0, h0 = w0 + 0.1, h0 + 0.1
      # patch_pos_embed = nn.functional.interpolate(
      #     patch_pos_embed.reshape([
      #         1, self.patch_embed.num_patches_w,
      #         self.patch_embed.num_patches_h, dim
      #     ]).transpose((0, 3, 1, 2)),
      #     scale_factor=(w0 / self.patch_embed.num_patches_w,
      #                   h0 / self.patch_embed.num_patches_h),
      #     mode='bicubic', )
  
      patch_pos_embed = nn.functional.interpolate(
          patch_pos_embed.reshape(
              [
                  1,
                  self.patch_embed.num_patches_w,
                  self.patch_embed.num_patches_h,
                  dim,
              ]
          ).transpose((0, 3, 1, 2)),
          (w0, h0),
          mode="bicubic",
      )
  
      assert (
          int(w0) == patch_pos_embed.shape[-2]
          and int(h0) == patch_pos_embed.shape[-1]
      )
      patch_pos_embed = patch_pos_embed.transpose((0, 2, 3, 1)).reshape(
          [1, -1, dim]
      )
      return paddle.concat(
          (class_pos_embed.unsqueeze(0), patch_pos_embed), axis=1
      )
  
imports: import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  from paddle.nn.initializer import Constant
  from ppdet.modeling.shape_spec import ShapeSpec
  from ppdet.core.workspace import register, serializable
  from .transformer_utils import zeros_, DropPath, Identity
label_desc: Use image node_id 4 for calling the VisionTransformer obj's underlying member method code with example usage: obj.interpolate_pos_encoding(x, w, h) and returns: paddle, self