class_docstr: Initializes weights as bilinear upsampling kernel.
  
  Example
  -------
  >>> # Given 'module', an instance of 'mxnet.module.Module',
  initialize weights to bilinear upsample...
  >>> init = mx.initializer.BilinearUpSample()
  >>> module.init_params(init)
  >>> for dictionary in module.get_params():
  ...     for key in dictionary:
  ...         print(key)
  ...         print(dictionary[key].asnumpy())
  ...
  fullyconnected0_weight
  [[ 0.  0.  0.]]
code: def _init_weight(self, _, arr):
      mx.nd.random.normal(0, 0.01, arr.shape, out=arr)
      f = math.ceil(arr.shape[2] / 2)
      c = (2 * f - 1 - f % 2) / (2.0 * f)
      for i in range(arr.shape[2]):
          for j in range(arr.shape[3]):
              arr[0, 0, i, j] = (1 - math.fabs(i / f - c)) * (
                  1 - math.fabs(j / f - c)
              )
      for c in range(1, arr.shape[0]):
          arr[c, 0, :, :] = arr[0, 0, :, :]
  
imports: from __future__ import absolute_import
  import warnings
  import math
  import mxnet
  from mxnet.context import cpu
  from mxnet.gluon import nn
  from mxnet.gluon import contrib
  from ..model_zoo import get_model
label_desc: Use image node_id 2 for calling the BilinearUpSample obj's underlying member method code with example usage: obj._init_weight(_, arr) without return types