class_docstr: `SVHN <http://ufldl.stanford.edu/housenumbers/>`_ Cropped Digit Dataset.
  Produces [img, label], img of 32x32x3 in range [0,255], label of 0-9
class_docstr_tok: ["`","SVHN","<","http",":","\/\/ufldl.stanford.edu\/housenumbers\/",">","`","_","Cropped","Digit","Dataset",".","Produces","[","img",",","label","]",",","img","of","32x32x3","in","range","[","0,255","]",",","label","of","0-9"]
code: def __iter__(self):
      n = self.X.shape[0]
      idxs = np.arange(n)
      if self.shuffle:
          self.rng.shuffle(idxs)
      for k in idxs:
          # since svhn is quite small, just do it for safety
          yield [self.X[k], self.Y[k]]
  
file_path: tensorpack/tensorpack/dataflow/dataset/svhn.py
imports: import numpy
  import os
  from ...utils import logger
  from ...utils.fs import download, get_dataset_path
  from ..base import RNGDataFlow
label_desc: Use image node_id 3 for calling the SVHNDigit obj's underlying member method code with example usage: obj.__iter__() without return types