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","."]
code: def forward(
      self, images: FloatTensor, sizes: IntTensor
  ) -> "OrderedDict[str, FloatTensor]":
      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 1 for calling the GridEmbedder obj's underlying member method code with example usage: obj.forward(images, sizes) without return types