code: def dilated_conv2d_test_sets():
      def _convert(input, kernel, output, kwargs):
          return [floatX(input), floatX(kernel), output, kwargs]
  
      input_shape = (3, 1, 11, 16)
      for dilation in (1, 2, 3):
          for filter_size in (1, 3):
              input = np.random.random(input_shape)
              kernel = np.random.random(
                  (16, 1, filter_size, filter_size)
              )
              kernel_flip = kernel[:, :, ::-1, ::-1]
              output = dilated_convNd(
                  input, kernel_flip, "valid", dilation, 2
              )
              yield _convert(
                  input, kernel, output, {"dilation": dilation}
              )
  
      # bias-less case
      input = np.random.random(input_shape)
      kernel = np.random.random((16, 1, 3, 3))
      output = dilated_convNd(
          input, kernel[:, :, ::-1, ::-1], pad="valid"
      )
      yield _convert(input, kernel, output, {"b": None})
      # untie_biases=True case
      yield _convert(input, kernel, output, {"untie_biases": True})
  
imports: import numpy
  import pytest
  import importlib
  import theano
  from theano import tensor
  import lasagne
  from lasagne.utils import floatX, as_tuple
label_desc: Use image node_id 11 for calling a global function with example usage: dilated_conv2d_test_sets() and returns: list