code: def create_video_pipe(
      video_files, sequence_length=8, target_size=224, stride=30
  ):
      pipeline = Pipeline(1, 4, 0, seed=42)
      with pipeline:
          images = fn.readers.video(
              device="gpu",
              filenames=video_files,
              sequence_length=sequence_length,
              stride=stride,
              shard_id=0,
              num_shards=1,
              random_shuffle=False,
              pad_last_batch=True,
              name="Reader",
          )
          images = fn.crop_mirror_normalize(
              images,
              dtype=types.FLOAT,
              output_layout="FCHW",
              crop=(target_size, target_size),
              mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
              std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
          )
  
          pipeline.set_outputs(images)
      return pipeline
  
file_path: DALI/docs/examples/use_cases/paddle/tsm/infer.py
imports: import argparse
  import json
  import os
  import paddle
  import paddle.static
  from nvidia.dali.pipeline import Pipeline
  import nvidia.dali.types
  import nvidia.dali.fn
  from nvidia.dali.plugin.paddle import DALIGenericIterator
  from tsm import TSM
  from utils import load_weights
label_desc: Use image node_id 1 for calling a global function with example usage: create_video_pipe(video_files, sequence_length, target_size, stride) and returns: pipeline