code: def _select_compilers_from_hardware(self):
      compilers = []
      if tensorflow_is_available():
          compilers.append(ModelCompiler.XLA)
          compilers.append(ModelCompiler.TFLITE)
      return compilers
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/optimizers/optimizers.py
imports: import platform
  from nebullvm.core.models import DeepLearningFramework, DeviceType, ModelCompiler
  from nebullvm.operations.optimizations.optimizers.base import Optimizer
  from nebullvm.operations.optimizations.compilers.utils import tvm_is_available, bladedisc_is_available, deepsparse_is_available, intel_neural_compressor_is_available, torch_tensorrt_is_available, onnxruntime_is_available, tensorrt_is_available, openvino_is_available, torch_neuron_is_available, torch_xla_is_available, faster_transformer_is_available
  from nebullvm.optional_modules.torch import torch
  from nebullvm.optional_modules.utils import torch_is_available, tensorflow_is_available, onnx_is_available
  from nebullvm.tools.utils import check_module_version
label_desc: Use image node_id 2 for calling the TensorflowOptimizer obj's underlying member method code with example usage: obj._select_compilers_from_hardware() and returns: compilers