class_docstr: `MS Coco Detection <https://cocodataset.org/>`_ Dataset.
  
  Args:
      root (string): Root directory where images are downloaded to.
      annFile (string): Path to json annotation file.
      transforms (callable, optional): A function/transform that takes input sample and its target as entry
          and returns a transformed version.
class_docstr_tok: ["`","MS","Coco","Detection","<","https",":","\/\/cocodataset.org\/",">","`","_","Dataset",".","Args",":","root","(","string",")",":","Root","directory","where","images","are","downloaded","to",".","annFile","(","string",")",":","Path","to","json","annotation","file",".","transforms","(","callable",",","optional",")",":","A","function\/transform","that","takes","input","sample","and","its","target","as","entry","and","returns","a","transformed","version","."]
code: def collate_fn(batch):
      return tuple(zip(*batch))
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/train_coco_dataset/my_dataset.py
imports: import os
  import json
  import torch
  from PIL import Image
  import torch.utils.data
  from pycocotools.coco import COCO
label_desc: Use image node_id 6 for calling the CocoDetection obj's underlying member method code with example usage: obj.collate_fn(batch) and returns: tuple