class_docstr: A unidirectional RNN encoder. Stacking should be performed as
  part of the cell.
  
  Params:
    resize_height: Resize the image to this height before feeding it
      into the convolutional network.
    resize_width: Resize the image to this width before feeding it
      into the convolutional network.
class_docstr_tok: ["A","unidirectional","RNN","encoder",".","Stacking","should","be","performed","as","part","of","the","cell",".","Params",":","resize_height",":","Resize","the","image","to","this","height","before","feeding","it","into","the","convolutional","network",".","resize_width",":","Resize","the","image","to","this","width","before","feeding","it","into","the","convolutional","network","."]
code: def __init__(self, params, mode, name="image_encoder"):
      super(InceptionV3Encoder, self).__init__(params, mode, name)
  
file_path: seq2seq/seq2seq/encoders/image_encoder.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3_base
  from seq2seq.encoders.encoder import Encoder, EncoderOutput
label_desc: Use image node_id 1 to create a new InceptionV3Encoder object from inherited base classes: Encoder with example: obj = InceptionV3Encoder(params, mode, name)