class_docstr: Takes a list of inputs, each with a shape CHWN, and transposes
  to HWCN, then concatenates along the HWC axis.
class_docstr_tok: ["Takes","a","list","of","inputs",",","each","with","a","shape","CHWN",",","and","transposes","to","HWCN",",","then","concatenates","along","the","HWC","axis","."]
code: def configure(self, in_obj):
      # we expect a list of layers
      assert isinstance(in_obj, list)
  
      self.in_shapes = [l.out_shape for l in in_obj]
  
      self.num_elements = np.sum(np.prod(l.out_shape) for l in in_obj)
      self.out_shape = self.num_elements
  
      # store the number of channels from the layer shapes
      self.channels = [l.out_shape[0] for l in in_obj]
  
imports: from neon.layers.layer import Layer, ParameterLayer
  import numpy
  from neon.transforms import Softmax
  from neon.initializers.initializer import Constant
  import math
  from collections import OrderedDict
label_desc: Use image node_id 2 for calling the ConcatTranspose obj's underlying member method code with example usage: obj.configure(in_obj) without return types