code: def show_results(
      x: TensorImage,
      y: TensorImage,
      samples,
      outs,
      ctxs=None,
      max_n=10,
      figsize=None,
      **kwargs,
  ):
      if ctxs is None:
          ctxs = get_grid(
              3 * min(len(samples), max_n),
              ncols=3,
              figsize=figsize,
              title="Input/Target/Prediction",
          )
      for i in range(2):
          ctxs[i::3] = [
              b.show(ctx=c, **kwargs)
              for b, c, _ in zip(
                  samples.itemgot(i), ctxs[i::3], range(max_n)
              )
          ]
      ctxs[2::3] = [
          b.show(ctx=c, **kwargs)
          for b, c, _ in zip(outs.itemgot(0), ctxs[2::3], range(max_n))
      ]
      return ctxs
  
imports: from __future__ import annotations
  from packaging.version import parse
  from ..basics import *
  from .core import *
  from .data import *
  from .augment import *
  from .None import models
  import torchvision
  from ..callback.hook import num_features_model
label_desc: Use image node_id 29 for calling a global function with example usage: show_results(x, y, samples, outs, ctxs, max_n, figsize) and returns: ctxs