code: def show_batch(
      x: TensorImage,
      y: TensorImage,
      samples,
      ctxs=None,
      max_n=10,
      nrows=None,
      ncols=None,
      figsize=None,
      **kwargs,
  ):
      if ctxs is None:
          ctxs = get_grid(
              min(len(samples), max_n),
              nrows=nrows,
              ncols=ncols,
              figsize=figsize,
              double=True,
          )
      for i in range(2):
          ctxs[i::2] = [
              b.show(ctx=c, **kwargs)
              for b, c, _ in zip(
                  samples.itemgot(i), ctxs[i::2], range(max_n)
              )
          ]
      return ctxs
  
imports: from __future__ import annotations
  from ..torch_basics import *
  from ..data.all import *
  from .core import *
  import types
label_desc: Use image node_id 5 for calling a global function with example usage: show_batch(x, y, samples, ctxs, max_n, nrows, ncols, figsize) and returns: ctxs