code: def _Inputs(
      self, x=None, y=None, q=3.0, dtype=dtypes.float64, sizes=None
  ):
      x = [-100, -2, -2, 0, 2, 2, 2, 100] if x is None else x
      y = [0, 0, 1, 0, 0, 1, 0.5, 1] if y is None else y
      assert len(x) == len(y)
      sizes = sizes if sizes else [len(x)]
      logits = constant_op.constant(
          x, shape=sizes, dtype=dtype, name="logits"
      )
      targets = constant_op.constant(
          y, shape=sizes, dtype=dtype, name="targets"
      )
      losses = np.array(self._WeightedCrossEntropy(x, y, q)).reshape(
          *sizes
      )
      return logits, targets, q, losses
  
imports: import math
  import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import nn_impl
  import tensorflow.python.ops.nn_grad
  from tensorflow.python.platform import test
label_desc: Use image node_id 2 for calling the WeightedCrossEntropyTest obj's underlying member method code with example usage: obj._Inputs(x, y, q, dtype, sizes) and returns: logits,  targets,  q,  losses