class_docstr: Parallel block class. 
class_docstr_tok: ["Parallel","block","class","."]
docstr: Feature map interpolation. 
docstr_tok: ["Feature","map","interpolation","."]
code: def interpolate(self, x, scale_factor: float, size: Tuple[int, int]):
      """Feature map interpolation."""
      B, N, C = x.shape
      H, W = size
      _assert(N == 1 + H * W, "")
  
      cls_token = x[:, :1, :]
      img_tokens = x[:, 1:, :]
  
      img_tokens = img_tokens.transpose(1, 2).reshape(B, C, H, W)
      img_tokens = F.interpolate(
          img_tokens,
          scale_factor=scale_factor,
          recompute_scale_factor=False,
          mode="bilinear",
          align_corners=False,
      )
      img_tokens = img_tokens.reshape(B, C, -1).transpose(1, 2)
  
      out = torch.cat((cls_token, img_tokens), dim=1)
  
      return out
  
imports: from functools import partial
  from typing import Tuple, List, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, _assert, LayerNorm
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 4 for calling the ParallelBlock obj's underlying member method code with example usage: obj.interpolate(x, scale_factor, size) and returns: out