chore: import upstream snapshot with attribution

tensorflow/compiler/mlir/lite/README.md

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+# The new MLIR based TensorFlow to TensorFlow Lite converter
+
+This directory contains:
+
+1.  [MLIR](https://github.com/llvm/llvm-project/tree/main/mlir) dialects,
+    transformation passes and utilities for TensorFlow Lite.
+
+## API:
+
+The API for converting TensorFlow models to TensorFlow Lite will be through
+`tf.lite.TFLiteConverter`. All the conversion code is open sourced, and
+the API will be integrated soon.
+
+### The conversion process from TensorFlow to TensorFlow Lite includes the
+following major passes:
+
+- Import from GraphDef, in .pb or .pbtxt  format, into MLIR.
+- Raise to Control-flow-graph. Converts TF Control Flow dialect to TF dialect.
+- The Canonicalization pass iteratively applies canonicalization
+transformations in a greedy way until no further changes occur.
+Canonicalization includes constant folding.
+- The Legalize pass converts TensorFlow operations to TensorFlow Lite
+ones. The operations that cannot be mapped to TensorFlow Lite dialect
+are left as TensorFlow operations. Unsupported op handling follows the
+proposed TFLite mechanism.
+- Optimizations are performed in both the TF & TFLite dialect; aiming for small
+size and high performance (among the core value proposition of
+TensorFlow Lite models).
+- The Export pass writes out TensorFlow Lite FlatBuffer format. This pass
+operates on MLIR TensorFlow Lite dialect and is simple/direct translation.
+
+See
+https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/lite/tf_tfl_passes.cc
+for the full list of MLIR passes for conversion from TensorFlow to TensorFlow
+Lite.