class_docstr: this base class is used to save predictions to disk
  (and being called by a evaluator later).
  Predictor has minimum support of single gpu prediction.
class_docstr_tok: ["this","base","class","is","used","to","save","predictions","to","disk","(","and","being","called","by","a","evaluator","later",")",".","Predictor","has","minimum","support","of","single","gpu","prediction","."]
code: def to_ctx(self, data, ctx=0, dtype=None):
      if isinstance(data, dict):
          for key in data:
              if torch.is_tensor(data[key]):
                  if (
                      dtype is not None
                      and data[key].dtype == torch.float32
                  ):
                      data[key] = data[key].to(dtype)
                  data[key] = data[key].to(ctx)
          return data
      else:
          raise ValueError(
              "non-dict type of batch is not supported yet."
          )
  
imports: import os
  import random
  import json
  import numpy
  import torch
  import pickle
  import math
  from tqdm import tqdm
label_desc: Use image node_id 5 for calling the Predictor obj's underlying member method code with example usage: obj.to_ctx(data, ctx, dtype) and returns: data