*   `tflite::Interpreter::signature_keys`
       *   `tflite::Interpreter::signature_inputs`
       *   `tflite::Interpreter::signature_outputs`
       *   `tflite::Interpreter::input_tensor_by_signature`
       *   `tflite::Interpreter::output_tensor_by_signature`

    *  Similarly, the following signature runner functions in the TF Lite C API are no longer considered experimental:

       *    `TfLiteInterpreterGetSignatureCount`

Optick-glasses, and not such as is usually wrought with Putty, whereby
the edges of the Sand-holes being worn away, there are left all over the
Glass a numberless Company of very little convex polite Risings like
Waves. The edges also of the Prism and Lens, so far as they may make any
irregular Refraction, must be covered with a black Paper glewed on. And

		{"Or", Func, 22},
		{"Ordered", Type, 21},
	},
	"compress/bzip2": {
		{"(StructuralError).Error", Method, 0},
		{"NewReader", Func, 0},
		{"StructuralError", Type, 0},
	},
	"compress/flate": {
		{"(*ReadError).Error", Method, 0},
		{"(*WriteError).Error", Method, 0},
		{"(*Writer).Close", Method, 0},
		{"(*Writer).Flush", Method, 0},
		{"(*Writer).Reset", Method, 2},
		{"(*Writer).Write", Method, 0},

testing or for special case where a user intends to pass custom MLIR computation
through a TensorFlow graph with the intent of having custom tooling processing
it downstream (when targeting a different environment, like TensorFlow lite for
example).
The MLIR module is expected to have a main() function that will be used as an
entry point. The inputs to the operations will be passed as argument to the