code: def random_fprop_layer(in_shape, in_size, layer):
      dtypeu = np.float32
      inp_rng = [0.0, 1.0]
      inpa = np.random.random(in_size)
      inpa *= inp_rng[1] - inp_rng[0]
      inpa += inp_rng[0]
      inpa = inpa.astype(dtypeu)
      inp = layer.be.array(inpa)
      inp.lshape = in_shape
      return layer.fprop(inp).get().shape
  
imports: from utils import tensors_allclose, allclose_with_out
  import numpy
  import pytest
  from neon import NervanaObject
  from neon.initializers.initializer import Uniform
  from neon.layers.layer import Convolution, Deconvolution, Layer, Linear, Bias, BatchNorm
  from neon.layers.container import Sequential
label_desc: Use image node_id 4 for calling a global function with example usage: random_fprop_layer(in_shape, in_size, layer) and returns: layer