code: def forward(
      self,
      kpt_bases,
      sampler_feature,
      sampled_feature_stride,
      fpn_stride,
  ):
      # assert kpt_bases.size(1) == 2
      assert fpn_stride % sampled_feature_stride == 0
      stride = int(fpn_stride / sampled_feature_stride)
      padded_sampler_feature = self.pad_to_target_size(
          sampler_feature,
          stride * (kpt_bases.size(2) - 1) + 1,
          stride * (kpt_bases.size(3) - 1) + 1,
      )
      predictor_offsets = self.kpt_predictor(
          padded_sampler_feature,
          torch.cat((kpt_bases[:, 1:2], kpt_bases[:, 0:1]), dim=1)
          * stride
          + stride // 2,
          # kpt_bases[:, [1, 0], :, :].contiguous() * stride + stride // 2,
          stride,
      )
      return predictor_offsets
  
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 3 for calling the Predictor obj's underlying member method code with example usage: obj.forward(kpt_bases, sampler_feature, sampled_feature_stride, fpn_stride) and returns: predictor_offsets