{"Punct", Var, 0},
		{"Quotation_Mark", Var, 0},
		{"Radical", Var, 0},
		{"Range16", Type, 0},
		{"Range16.Hi", Field, 0},
		{"Range16.Lo", Field, 0},
		{"Range16.Stride", Field, 0},
		{"Range32", Type, 0},
		{"Range32.Hi", Field, 0},
		{"Range32.Lo", Field, 0},
		{"Range32.Stride", Field, 0},
		{"RangeTable", Type, 0},
		{"RangeTable.LatinOffset", Field, 1},

any one Interval of the Teeth, so that the Range of Colours which comes
from thence may be taken away, and you will see the Light of the rest of
the Ranges to be expanded into the Place of the Range taken away, and
there to be coloured. Let the intercepted Range pass on as before, and
its Colours falling upon the Colours of the other Ranges, and mixing
with them, will restore the Whiteness.

This op determines the maximum scale_factor that would map the initial
[input_min, input_max] range to a range that lies within the representable
quantized range.

It determines the scale from one of input_min and input_max, then updates the
other one to maximize the representable range.

e.g.

*   if the output is signed, num_bits = 8, [input_min, input_max] = [-10.0,

    *   Experimental support for lowering `list_ops.tensor_list_set_item` with
        `DynamicUpdateSlice`.
    *   Enabled a new MLIR-based dynamic range quantization backend by default
        *   The new backend is used for post-training int8 dynamic range
            quantization and post-training float16 quantization.
        *   Set `experimental_new_dynamic_range_quantizer` in