code: def pad_to_target_size(self, features, target_h, target_w):
      N, C, H, W = features.size()
      pad_h = max(0, target_h - H)
      pad_w = max(0, target_w - W)
      return F.pad(features, [0, pad_w, 0, pad_h])
  
imports: import math
  from typing import List, Dict
  import torch
  from torch import nn
  from torch.nn import functional
  from torch.autograd import Variable
  import numpy
  from ...utils.comm import compute_locations
  from ...nn.shape_spec import ShapeSpec
  from ...nn.batch_norm import NaiveSyncBatchNorm
  from ...nn.deform_conv import DeformConvWithChangeableStride
  from ...nn.group_norm import NaiveGroupNorm
  from .directpose_outputs import DirectPoseOutputs
label_desc: Use image node_id 2 for calling the Predictor obj's underlying member method code with example usage: obj.pad_to_target_size(features, target_h, target_w) and returns: F