Adding ReadMe for Layer by Layer Debug Targets

tensorflow/lite/micro/tools/Debugging_Tool_README.md

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+# C++ Layer by Layer Output Tool on TFLite Micro
+
+This tool allows you to pass in a TfLite model and returns a flatbuffer file with input and the corresponding output values appended into it that can be passed into a python debugging tool which can compare those golden values vs the x86 TFLM reference kernel implementation.
+
+The C++ Tool/binary will write a debugging flatbuffer to the path provide in 2nd arg
+using the tflite_model provided in the 1st arg 
+
+##### Command bazel/blaze:
+
+```
+ bazel run tensorflow/lite/micro/tools:layer_cc -- \
+    </path/to/input_model.tflite>
+   </path/to/output.file_name>
+```
+
+##### How to Build using Makefile :
+
+```
+make -f tensorflow/lite/micro/tools/make/Makefile layer_by_layer_output_tool -j24
+```
+
+# Python Layer by Layer Debugging Tool 
+
+The Python Tool/Script can first be used to comapre TFLM vs Tflite Ouputs for random Inputs by only providing a TfLite file
+
+#### TfLite vs TFLM command:
+``` 
+ bazel run tensorflow/lite/micro/tools:layer_by_layer_debugger -- \
+    --input_tflite_file=</path/to/my_model.tflite>
+```
+
+The Python Tool/Script can also be used to comapre TFLM vs Expected Output/Flatbuffer provided by the C++ Tool/binary. 
+
+#### TFLM vs Expected Command:
+``` 
+  bazel run tensorflow/lite/micro/tools:layer_by_layer_debugger -- \
+    --input_tflite_file=</path/to/my_model.tflite> \
+    --layer_by_layer_data_file=</path/to/my_debug_flatbuffer_file>
+```
+
+#### Optional Flags:
+ ` --print_dump  `
+when this flags is set it will dump a part of the TFLM and Ouput it's compared against for each layer
+
+ ` --rng`
+integer flag that only works in TfLite vs TFLM comparison(when only a TfLite Model is Provided).It can be used to set the rng seed to a differen value then it's default value of 42.
+