{"(*Reader).Next", Method, 0},
		{"(*Reader).Seek", Method, 0},
		{"(*Reader).SeekPC", Method, 7},
		{"(*Reader).SkipChildren", Method, 0},
		{"(*StructType).Common", Method, 0},
		{"(*StructType).Defn", Method, 0},
		{"(*StructType).Size", Method, 0},
		{"(*StructType).String", Method, 0},
		{"(*TypedefType).Common", Method, 0},
		{"(*TypedefType).Size", Method, 0},
		{"(*TypedefType).String", Method, 0},

  let description = [{
So, for example, in the following code

  ```python

  # This input will be captured.
  y = tf.placeholder_with_default(1.0, shape=[])

  @tf.Defun(tf.float32)
  def computation(a):
    return tf.matmul(a, a) + y

  b = gen_batch_ops.batch_function(
          f=computation
          in_tensors=[a],
          captured_tensors=computation.captured_inputs,

	FDIVD (BX), F0                          // d833 or dc33
	FDIVD (R11), F0                         // 41d833 or 41dc33
	//TODO: FDIVRP F0, F2                   // defa
	//TODO: FDIVRP F0, F3                   // defb
	//TODO: FDIVR F2, F0                    // d8fa
	//TODO: FDIVR F3, F0                    // d8fb
	//TODO: FDIVFR (BX)                     // d83b

*   `tf.saved_model.SaveOptions`

    * `SaveOptions` now takes a new argument called
      `experimental_debug_stripper`. When enabled, this strips the debug nodes
      from both the node defs and the function defs of the graph. Note that
      this currently only strips the `Assert` nodes from the graph and
      converts them into `NoOp`s instead.

*   `tf.data`

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Fh/6Lg00QKwJKPf0kd1Us6SfqklxGczOaWNLyiM7JthFRNMp0qVX6NjLqGoCNO+d
/+nNk6s2x4rLECj/EROmE3ZQQEo5nBXmPlhXpVem23rGfXEQvXDNqFmvqrP+Befn
/+aDpo89QIm3sE8G0LfgcajIdSfgLH+NJTvOVAtXXVXJPK39Njr1aBzWTbWhLS2b
ji7DwP7hshdh7DE2rS623vlzvkkrms8oKkiRpKATdhQ8CEx+mhTFKCj6GtNqhwtt
Cbf98N9GpiHD0has65YtgQQjk2pLR62rZf6czagRfKbFQzXjl2JxS/bsHVhTkhyJ