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 fprop(self, inputs):
      start = 0
      for layer, num_channels in zip(inputs, self.channels):
          # reshape (C, HW, N)
          rlayer = layer.reshape((num_channels, -1, self.be.bsz))
  
          # transpose to (HW, C, N) and store in buffer
          C, HW, N = rlayer.shape
          end = start + C * HW
          output_view = self.outputs[start:end, :].reshape((HW, C, N))
  
          self.be.copy_transpose(rlayer, output_view, axes=(1, 0, 2))
          start = end
  
      return self.outputs
  
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 4 for calling the ConcatTranspose obj's underlying member method code with example usage: obj.fprop(inputs) and returns: self