{"ValueSpec.Doc", Field, 0},
		{"ValueSpec.Names", Field, 0},
		{"ValueSpec.Type", Field, 0},
		{"ValueSpec.Values", Field, 0},
		{"Var", Const, 0},
		{"Visitor", Type, 0},
		{"Walk", Func, 0},
	},
	"go/build": {
		{"(*Context).Import", Method, 0},
		{"(*Context).ImportDir", Method, 0},
		{"(*Context).MatchFile", Method, 2},
		{"(*Context).SrcDirs", Method, 0},

2F8E          ; mapped                 ; 8840          # 3.0  KANGXI RADICAL BLOOD
2F8F          ; mapped                 ; 884C          # 3.0  KANGXI RADICAL WALK ENCLOSURE
2F90          ; mapped                 ; 8863          # 3.0  KANGXI RADICAL CLOTHES
2F91          ; mapped                 ; 897E          # 3.0  KANGXI RADICAL WEST

    *   Code that requires very tricky shape manipulation via converted op
        layers in order to work, where the Keras symbolic shape inference proves
        insufficient.
    *   Code that tries manually walking a `tf.keras.Model` layer by layer and
        assumes layers only ever have one positional argument. This assumption
        doesn't hold true before TF 2.4 either, but is more likely to cause
        issues now.

ellipsis_mask = 1<<3 = 8
new_axis_mask = 1<<2 = 4
shrink_axis_mask = 1<<0 = 1
```

In this case if `foo.shape` is (5, 5, 5, 5, 5, 5) the final shape of
the slice becomes (2, 1, 5, 5, 2, 5).
Let us walk step by step through each argument specification.

1.  The first argument in the example slice is turned into `begin = 1` and
`end = begin + 1 = 2`. To disambiguate from the original spec `2:4` we