class_docstr: Stitch two images with overlapping fields of view.
  
  Args:
      matcher: image feature matching module.
      estimator: method to compute homography, either "vanilla" or "ransac".
          "ransac" is slower with a better accuracy.
      blending_method: method to blend two images together.
          Only "naive" is currently supported.
  
  Note:
      Current implementation requires strict image ordering from left to right.
  
  .. code-block:: python
  
      IS = ImageStitcher(KF.LoFTR(pretrained='outdoor'), estimator='ransac').cuda()
      # Compute the stitched result with less GPU memory cost.
      with torch.inference_mode():
          out = IS(img_left, img_right)
      # Show the result
      plt.imshow(K.tensor_to_image(out))
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code: def on_matcher(self, data: Dict[str, Tensor]) -> Dict[str, Tensor]:
      return self.matcher(data)
  
file_path: kornia/kornia/contrib/image_stitching.py
imports: from typing import Dict, Optional, Tuple
  import torch
  from kornia.color import rgb_to_grayscale
  from kornia.core import Module, Tensor, concatenate, where, zeros_like
  from kornia.feature import LocalFeatureMatcher, LoFTR
  from kornia.geometry.homography import find_homography_dlt_iterated
  from kornia.geometry.ransac import RANSAC
  from kornia.geometry.transform import warp_perspective
label_desc: Use image node_id 7 for calling the ImageStitcher obj's underlying member method code with example usage: obj.on_matcher(data) and returns: self