class_docstr: Provide the personality in the `text` field, and the captions in the `labels` field.
  
  To specify your own path to the YFCC100m images, please use the `--yfcc-path`
  command line argument.
class_docstr_tok: ["Provide","the","personality","in","the","`","text","`","field",",","and","the","captions","in","the","`","labels","`","field",".","To","specify","your","own","path","to","the","YFCC100m","images",",","please","use","the","`","--","yfcc-path","`","command","line","argument","."]
code: def __init__(self, opt, shared=None):
      super().__init__(opt, shared)
      self.opt = opt
      self.image_mode = opt.get("image_mode", "no_image_model")
      self.data_path, personalities_data_path, self.image_path = _path(
          opt
      )
      self.datatype = opt.get("datatype").split(":")[0]
      self.include_personality = opt.get("include_personality")
      self.include_image = opt.get("include_image")
      self.num_test_labels = opt.get("num_test_labels")
      if shared and "data" in shared:
          self.data = shared["data"]
          self.image_loader = shared["image_loader"]
      else:
          self.image_loader = ImageLoader(opt)
          self._setup_data(self.data_path, personalities_data_path)
      self.reset()
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  from parlai.core.teachers import FixedDialogTeacher
  from parlai.core.image_featurizers import ImageLoader
  from parlai.utils.io import PathManager
  from .build import build
  import json
  import os
label_desc: Use image node_id 1 to create a new PersonalityCaptionsTeacher object from inherited base classes: FixedDialogTeacher with example: obj = PersonalityCaptionsTeacher(opt, shared)