class_docstr: This layer concatenates two input tensors,
  the first one is cropped and resized to match the second one.
  
  This layer assumes the same amount of differences
  in every spatial dimension in between the two tensors.
code: def __init__(self, name="crop_concat"):
      Layer.__init__(self, name=name)
  
imports: from __future__ import absolute_import, print_function
  import tensorflow
  from niftynet.layer.base_layer import TrainableLayer, Layer
  from niftynet.layer.convolution import ConvolutionalLayer
  from niftynet.layer.crop import CropLayer
  from niftynet.layer.deconvolution import DeconvolutionalLayer
  from niftynet.layer.downsample import DownSampleLayer
  from niftynet.layer.elementwise import ElementwiseLayer
  from niftynet.layer.linear_resize import LinearResizeLayer
  from niftynet.network.base_net import BaseNet
label_desc: Use image node_id 1 to create a new CropConcat object from inherited base classes: Layer with example: obj = CropConcat(name)