chore: import upstream snapshot with attribution

tools/tensorflow-quantization/docs/source/globals.rst

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+.. _globals_api:
+
+**tensorflow_quantization**
+============================
+
+
+:tensorflow_quantization.G_NUM_BITS:
+
+    8 bit quantization is used by default. However, it can be changed by using ``G_NUM_BITS`` global variable.
+    The following code snippet performs 4 bit quantization.
+
+    .. code:: python
+
+        import tensorflow_quantization
+        # get pretrained model
+        .....
+
+        # perform 4 bit quantization
+        tensorflow_quantization.G_NUM_BITS = 4
+        q_model = quantize_model(nn_model_original)
+
+        # fine-tune model
+        .....
+        
+    Check ``test_end_to_end_workflow_4bit()`` test case from ``quantize_test.py`` test module.
+
+:tensorflow_quantization.G_NARROW_RANGE:
+
+    If True, the absolute value of quantized minimum is the same as the quantized maximum value. For example,
+    minimum of -127 is used for 8 bit quantization instead of -128. TensorRT |tred| only supports G_NARROW_RANGE=True.
+
+:tensorflow_quantization.G_SYMMETRIC:
+
+    If True, 0.0 is always in the center of real min, max i.e. zero point is always 0. 
+    TensorRT |tred| only supports G_SYMMETRIC=True.
+
+.. attention:: When used, set global variables immediately before the ``quantize_model`` function call.
+
+.. |tred|    unicode:: U+2122 .. TRADEMARK SIGN