code: def _get_model_analysis_input(cfg, use_train_input):
      """
      Return a dummy input for model analysis with batch size 1. The input is
          used for analyzing the model (counting flops and activations etc.).
      Args:
          cfg (CfgNode): configs. Details can be found in
              slowfast/config/defaults.py
          use_train_input (bool): if True, return the input for training. Otherwise,
              return the input for testing.
  
      Returns:
          inputs: the input for model analysis.
      """
      rgb_dimension = 3
      if use_train_input:
          if "imagenet" in cfg.TRAIN.DATASET:
              input_tensors = torch.rand(
                  rgb_dimension,
                  cfg.DATA.TRAIN_CROP_SIZE,
                  cfg.DATA.TRAIN_CROP_SIZE,
              )
          else:
              input_tensors = torch.rand(
                  rgb_dimension,
                  cfg.DATA.NUM_FRAMES,
                  cfg.DATA.TRAIN_CROP_SIZE,
                  cfg.DATA.TRAIN_CROP_SIZE,
              )
      else:
          if "imagenet" in cfg.TEST.DATASET:
              input_tensors = torch.rand(
                  rgb_dimension,
                  cfg.DATA.TEST_CROP_SIZE,
                  cfg.DATA.TEST_CROP_SIZE,
              )
          else:
              input_tensors = torch.rand(
                  rgb_dimension,
                  cfg.DATA.NUM_FRAMES,
                  cfg.DATA.TEST_CROP_SIZE,
                  cfg.DATA.TEST_CROP_SIZE,
              )
      model_inputs = pack_pathway_output(cfg, input_tensors)
      for i in range(len(model_inputs)):
          model_inputs[i] = model_inputs[i].unsqueeze(0)
          if cfg.NUM_GPUS:
              model_inputs[i] = model_inputs[i].cuda(non_blocking=True)
  
      # If detection is enabled, count flops for one proposal.
      if cfg.DETECTION.ENABLE:
          bbox = torch.tensor([[0, 0, 1.0, 0, 1.0]])
          if cfg.NUM_GPUS:
              bbox = bbox.cuda()
          inputs = (model_inputs, bbox)
      else:
          inputs = (model_inputs,)
      return inputs
  
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 5 for calling a global function with example usage: _get_model_analysis_input(cfg, use_train_input) and returns: inputs