code: def CheckGradConfigsToTestExplicit():
      """Iterator for different convolution shapes, strides and explicit paddings.
  
      compute_gradient_error() is very expensive. So the configs should be
      relatively small.
  
      Returns:
        List of tuples (input_size, filter_size, out_size, stride, padding,
        dilations), the depthwise convolution parameters.
      """
  
      def Config(
          input_size,
          filter_size,
          out_size,
          stride=1,
          padding=None,
          dilations=None,
      ):
          return (
              input_size,
              filter_size,
              out_size,
              stride,
              padding,
              dilations,
          )
  
      return [
          Config(
              [2, 5, 8, 1],
              [4, 4, 1, 2],
              [2, 3, 10, 2],
              padding=[[0, 1], [2, 3]],
          ),
          Config(
              [4, 5, 5, 1],
              [2, 2, 1, 2],
              [4, 4, 5, 2],
              2,
              padding=[[3, 1], [5, 0]],
          ),
          Config(
              [2, 4, 4, 2],
              [3, 1, 2, 2],
              [2, 7, 11, 4],
              padding=[[4, 1], [3, 4]],
          ),
          Config(
              [1, 15, 15, 2],
              [1, 3, 2, 1],
              [1, 18, 23, 2],
              padding=[[3, 0], [2, 8]],
          ),
          Config(
              [2, 15, 16, 1],
              [3, 3, 1, 2],
              [2, 5, 8, 2],
              3,
              padding=[[0, 0], [10, 0]],
          ),
          Config(
              [2, 5, 8, 1],
              [3, 4, 1, 2],
              [2, 5, 10, 2],
              padding=[[3, 1], [2, 3]],
              dilations=[2, 1],
          ),
          # These cases test the kernels in depthwise_conv_op_gpu.h which are used
          # if the input size is small.
          Config(
              [2, 4, 3, 2],
              [3, 2, 2, 1],
              [2, 4, 3, 2],
              padding=[[2, 0], [1, 0]],
          ),
      ]
  
imports: import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import nn_impl
  from tensorflow.python.ops import nn_ops
  import tensorflow.python.ops.nn_grad
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 6 for calling a global function with example usage: CheckGradConfigsToTestExplicit() and returns: list, input_size,  filter_size,  out_size,  stride,  padding,  dilations