class_docstr: `MS Coco Detection <https://cocodataset.org/>`_ Dataset.
  
  Args:
      root (string): Root directory where images are downloaded to.
      dataset (string): train or val.
      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",".","dataset","(","string",")",":","train","or","val",".","transforms","(","callable",",","optional",")",":","A","function\/transform","that","takes","input","sample","and","its","target","as","entry","and","returns","a","transformed","version","."]
code: def get_height_and_width(self, index):
      coco = self.coco
      img_id = self.ids[index]
  
      img_info = coco.loadImgs(img_id)[0]
      w = img_info["width"]
      h = img_info["height"]
      return h, w
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/mask_rcnn/my_dataset_coco.py
imports: import os
  import json
  import torch
  from PIL import Image
  import torch.utils.data
  from pycocotools.coco import COCO
  from train_utils import coco_remove_images_without_annotations, convert_coco_poly_mask
label_desc: Use image node_id 5 for calling the CocoDetection obj's underlying member method code with example usage: obj.get_height_and_width(index) and returns: h,  w