code: def sample_padded_inliers(
      xsamples: Tensor,
      ysamples: Tensor,
      inlier_counts: Tensor,
      inl_ransidx: Tensor,
      inl_sampleidx: Tensor,
      numransacs: int,
      dv: torch.device,
  ) -> Tuple[Tensor, Tensor]:
      maxinliers = int(torch.max(inlier_counts).item())
      dtype = xsamples.dtype
      padded_inlier_x = torch.zeros(
          size=(numransacs, maxinliers, 2), device=dv, dtype=dtype
      )
      padded_inlier_y = torch.zeros(
          size=(numransacs, maxinliers, 2), device=dv, dtype=dtype
      )
  
      padded_inlier_x[
          inl_ransidx, piecewise_arange(inl_ransidx)
      ] = xsamples[inl_sampleidx]
      padded_inlier_y[
          inl_ransidx, piecewise_arange(inl_ransidx)
      ] = ysamples[inl_sampleidx]
  
      return padded_inlier_x, padded_inlier_y
  
file_path: kornia/kornia/feature/adalam/ransac.py
imports: from typing import Any, Dict, Tuple, Union
  import torch
  from kornia.core import Tensor
  from .utils import arange_sequence, batch_2x2_ellipse, batch_2x2_inv, draw_first_k_couples, piecewise_arange
label_desc: Use image node_id 4 for calling a global function with example usage: sample_padded_inliers(xsamples, ysamples, inlier_counts, inl_ransidx, inl_sampleidx, numransacs, dv) and returns: padded_inlier_x,  padded_inlier_y