class_docstr: SSD model is like a Tree, except with additional handling for the output layer, and
  for the prior boxes
class_docstr_tok: ["SSD","model","is","like","a","Tree",",","except","with","additional","handling","for","the","output","layer",",","and","for","the","prior","boxes"]
code: def distribute_tensors(self, x, parallelism="Disabled"):
      for keys in x.keys():
          for tensor in x[keys]:
              altered_tensor = self.be.distribute_data(
                  tensor, parallelism
              )
              if (
                  altered_tensor is not None
              ):  # if altered, track it so we can revert later.
                  self.altered_tensors.append(altered_tensor)
  
imports: from neon.initializers import Constant, Xavier
  from neon.transforms import Rectlin
  from neon.layers import Conv, Pooling, BranchNode
  from layer import Normalize, PriorBox, DetectionOutput, ConcatTranspose
  import numpy
  from neon.util.persist import load_obj
  from neon.layers.container import Tree
  from neon.layers.layer import Layer
  from collections import OrderedDict
  import inspect
  import re
  from neon.data.datasets import Dataset
  import os
label_desc: Use image node_id 14 for calling the SSD obj's underlying member method code with example usage: obj.distribute_tensors(x, parallelism) without return types