# Verify that we match correctly the input / output when they are scalar.

# CHECK-LABEL: func @main
# CHECK-SAME:  (%{{[a-z0-9]+}}: tensor<f32> {tf.device = "/device:CPU:0"})
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "input"
# CHECK-SAME:  outputs = "out:1,out"

      else:
        # Op has been registered, then we don't need to generate register code.
        return

    # Validates the function inputs match what are in the decorator.
    inputs = all_dec_args.get('inputs', [])
    attrs = all_dec_args.get('attrs', [])
    expected_args = [arg.split(':')[0] for arg in inputs + attrs]
    all_func_args = self.visit(node.args)

    if len(expected_args) != len(all_func_args):
      raise KeyError(

               op_name,
               inputs=None,
               attrs=None,
               derived_attrs=None,
               outputs=None):
    self._op_name = op_name
    self._inputs = inputs
    self._attrs = attrs
    self._derived_attrs = derived_attrs
    self._outputs = outputs

  def __call__(self, compose_fn):
    # TODO(fengliuai): more sanity check of the input function and make sure

	// swap in and out to exercise copy-up and copy-down
	input8, output8 = output8, input8
	input16, output16 = output16, input16
	input32, output32 = output32, input32
	input64, output64 = output64, input64
	in := 0
	out := 13
	for i := range input8 {
		input8[i] = u8(in)
		output8[i] = u8(out)
		outputS[i] = u8(out)
		input16[i] = u16(in)
		output16[i] = u16(out)
		input32[i] = u32(in)
		output32[i] = u32(out)

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