class_docstr: A `GridEmbedder` takes a batch of images as a tensor with shape
  `(batch_size, color_channels, height, width)`, and returns an ordered dictionary
  of tensors with shape `(batch_size, *)`, each representing a specific feature.
class_docstr_tok: ["A","`","GridEmbedder","`","takes","a","batch","of","images","as","a","tensor","with","shape","`","(","batch_size",",","color_channels",",","height",",","width",")","`",",","and","returns","an","ordered","dictionary","of","tensors","with","shape","`","(","batch_size",",","*",")","`",",","each","representing","a","specific","feature","."]
docstr: Returns the feature names, in order, i.e. the keys of the ordered output
  dictionary from `.forward()`.
docstr_tok: ["Returns","the","feature","names",",","in","order",",","i.e",".","the","keys","of","the","ordered","output","dictionary","from","`",".forward","(",")","`","."]
code: def get_feature_names(self) -> Tuple[str, ...]:
      """
      Returns the feature names, in order, i.e. the keys of the ordered output
      dictionary from `.forward()`.
      """
      raise NotImplementedError()
  
file_path: allennlp/allennlp/modules/vision/grid_embedder.py
imports: from collections import OrderedDict
  from typing import Tuple
  from torch import nn, FloatTensor, IntTensor
  import torchvision
  from allennlp.common.registrable import Registrable
label_desc: Use image node_id 2 for calling the GridEmbedder obj's underlying member method code with example usage: obj.get_feature_names() without return types