The issue was due to a difference between general tflite implementations and specifically tflm implementations. TfLite will not specify the dimensions of the output tensors prior to the model being invoked and instead relies on dynamically allocating the necessary space when the model is invoked. TFLM does not support dyanmic allocation and instead relies on the predefined dimensions from the metadata of the tflite model to statically allocate. I used netron.app to determine that this metadata was missing. I used the flatbuffer compiler to convert the .tflite file to a .json file where I could see and manipulate the metadata:

.\flatc.exe -t --strict-json --defaults-json -o . schema.fbs -- model2.tflite

I added the missing dimensions to the output tensors and then recompiled from the json back into a .tflite file:

flatc -b --defaults-json -o new_model schema.fbs model2.json

Make sure to have all the proper file paths, I put all of mine in the same folder.