code: def plot_input(tensor, bboxes=(), texts=(), path="./tmp_vis.png"):
      """
      Plot the input tensor with the optional bounding box and save it to disk.
      Args:
          tensor (tensor): a tensor with shape of `NxCxHxW`.
          bboxes (tuple): bounding boxes with format of [[x, y, h, w]].
          texts (tuple): a tuple of string to plot.
          path (str): path to the image to save to.
      """
      tensor = tensor.float()
      tensor = tensor - tensor.min()
      tensor = tensor / tensor.max()
      f, ax = plt.subplots(
          nrows=1, ncols=tensor.shape[0], figsize=(50, 20)
      )
      for i in range(tensor.shape[0]):
          ax[i].axis("off")
          ax[i].imshow(tensor[i].permute(1, 2, 0))
          # ax[1][0].axis('off')
          if bboxes is not None and len(bboxes) > i:
              for box in bboxes[i]:
                  x1, y1, x2, y2 = box
                  ax[i].vlines(
                      x1, y1, y2, colors="g", linestyles="solid"
                  )
                  ax[i].vlines(
                      x2, y1, y2, colors="g", linestyles="solid"
                  )
                  ax[i].hlines(
                      y1, x1, x2, colors="g", linestyles="solid"
                  )
                  ax[i].hlines(
                      y2, x1, x2, colors="g", linestyles="solid"
                  )
  
          if texts is not None and len(texts) > i:
              ax[i].text(0, 0, texts[i])
      f.savefig(path)
  
imports: import json
  import logging
  import math
  import numpy
  import os
  from datetime import datetime
  import psutil
  import torch
  import torchvision.io
  from fvcore.nn.activation_count import activation_count
  from fvcore.nn.flop_count import flop_count
  from matplotlib import pyplot
  from torch import nn
  from torchvision.utils import make_grid
  import slowfast.utils.logging
  import slowfast.utils.multiprocessing
  from slowfast.datasets.utils import pack_pathway_output
  from slowfast.models.batchnorm_helper import SubBatchNorm3d
  from slowfast.utils.env import pathmgr
label_desc: Use image node_id 9 for calling a global function with example usage: plot_input(tensor, bboxes, texts, path) without return types