code: def __init__(self):
      super().__init__()
      self.pipeline_dl_framework = DeepLearningFramework.TENSORFLOW
  
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 1 to create a new TensorflowOptimizer object from inherited base classes: Optimizer with example: obj = TensorflowOptimizer()