Adding ReadMe for Layer by Layer Debug Targets

debugging_output.md

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+The TFLM debugging output tools allow TFLM users to easily debug their models
+by providing a tool that will compare the intermediate  values(output of each OP/Kernel)
+from a model post invoke between the TFLM and TfLite. As well as a way to
+compare intermediate values between TFLM x86 implementations and Optimized 
+Implementations.
+
+
+The workflow can be divided into two parts:
+
+The first is a C++ binary that takes a TfLite model and returns a file that has
+random inputs and their corresponding output values for each layer of the model
+it was provided. 
+
+The second is a python script that takes a TfLite model and optionally can take
+ the file outputted by the C++ binary mentioned above. When only the TfLite
+ model is provided as input, the script generates random input and compares
+ TFLM vs TfLite inference outputs for each layer of the model. When the file
+ from the C++ binary is provided alongside the TfLite model as inputs , the
+ script runs TFLM x86 inference comparison to the expected output. 
+
+
+
+
+
+
+# C++ Excpected Layer by Layer Output Tool on TFLite Micro
+
+This C++ binary 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 file 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 outputs 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 compare, TFLM's python x86 output
+vs expected output 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 flag is set, it will print the TFLM output for each layer that is
+compared.
+
+ ` --rng`
+Integer random number seed for generating input data for comparisons against TFLite. (Default: 42)