class_docstr: Vision Transformer with support for patch input
      
class_docstr_tok: ["Vision","Transformer","with","support","for","patch","input"]
code: def forward(self, x):
      x = x["image"] if isinstance(x, dict) else x
      _, _, h, w = x.shape
  
      x = self.patch_embed(x)
  
      B, D, Hp, Wp = x.shape  # b * c * h * w
  
      cls_tokens = self.cls_token.expand(
          (B, self.cls_token.shape[-2], self.cls_token.shape[-1])
      )
      x = x.flatten(2).transpose([0, 2, 1])  # b * hw * c
      x = paddle.concat([cls_tokens, x], axis=1)
  
      if self.pos_embed is not None:
          # x = x + self.interpolate_pos_encoding(x, w, h)
          x = x + self.interpolate_pos_encoding(x, h, w)
  
      x = self.pos_drop(x)
  
      rel_pos_bias = (
          self.rel_pos_bias() if self.rel_pos_bias is not None else None
      )
  
      feats = []
      for idx, blk in enumerate(self.blocks):
          if self.use_checkpoint and self.training:
              x = paddle.distributed.fleet.utils.recompute(
                  blk, x, rel_pos_bias, **{"preserve_rng_state": True}
              )
          else:
              x = blk(x, rel_pos_bias)
  
          if idx in self.out_indices:
              xp = paddle.reshape(
                  paddle.transpose(
                      self.norm(x[:, 1:, :]), perm=[0, 2, 1]
                  ),
                  shape=[B, D, Hp, Wp],
              )
              feats.append(xp)
  
      if self.with_fpn:
          fpns = [self.fpn1, self.fpn2, self.fpn3, self.fpn4][
              -self.num_fpn_levels :
          ]
          assert len(fpns) == len(feats) or len(feats) == 1, ""
          outputs = []
          for i, m in enumerate(fpns):
              outputs.append(
                  m(feats[i] if len(feats) == len(fpns) else feats[-1])
              )
  
          return outputs
  
      return feats
  
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 7 for calling the VisionTransformer obj's underlying member method code with example usage: obj.forward(x) and returns: feats, outputs