code: def test_tensorflow_tflite():
      model = ResNet50()
      input_data = [
          ((tf.random.normal([1, 224, 224, 3]),), 0) for i in range(100)
      ]
  
      # Run nebullvm optimization in one line of code
      optimized_model = optimize_model(
          model,
          input_data=input_data,
          ignore_compilers=[
              compiler
              for compiler in COMPILER_LIST
              if compiler != "tflite"
          ],
          ignore_compressors=[
              compressor for compressor in COMPRESSOR_LIST
          ],
          metric_drop_ths=0.1,
      )
  
      # Try the optimized model
      x = tf.random.normal([1, 224, 224, 3])
      res_original = model.predict(x)
      res_optimized = optimized_model.predict(x)[0]
  
      assert isinstance(optimized_model, TFLiteBackendInferenceLearner)
      assert abs((res_original - res_optimized)).max() < 1e-2
  
file_path: nebuly/optimization/speedster/speedster/api/tests/test_tensorflow.py
imports: from tempfile import TemporaryDirectory
  import cpuinfo
  import pytest
  import tensorflow
  from keras.applications import ResNet50
  from nebullvm.config import COMPILER_LIST, COMPRESSOR_LIST
  from nebullvm.operations.inference_learners.onnx import TensorflowONNXInferenceLearner
  from nebullvm.operations.inference_learners.openvino import TensorflowOpenVinoInferenceLearner
  from nebullvm.operations.inference_learners.tensor_rt import TensorflowONNXTensorRTInferenceLearner
  from nebullvm.operations.inference_learners.tensorflow import TensorflowBackendInferenceLearner, TFLiteBackendInferenceLearner
  from nebullvm.operations.inference_learners.tvm import TensorflowApacheTVMInferenceLearner
  from nebullvm.operations.optimizations.compilers.utils import tvm_is_available
  from nebullvm.tools.utils import gpu_is_available
  from speedster import optimize_model, load_model
label_desc: Use image node_id 3 for calling a global function with example usage: test_tensorflow_tflite() without return types