code: def from_keras(cls, keras_model, dataset):
      import tensorflow.keras.backend as K
  
      sess = K.get_session()
  
      outputs = keras_model.outputs
      inputs = keras_model.inputs
  
      check_data_compatible(dataset, keras_model, mode="inference")
  
      if isinstance(dataset, TFNdarrayDataset):
          dataset = _standarize_feature_dataset(dataset, keras_model)
  
      return cls(sess, outputs, inputs, dataset)
  
file_path: analytics-zoo/pyzoo/zoo/tfpark/tf_predictor.py
imports: import sys
  from zoo.pipeline.api.net.utils import find_placeholders, _check_the_same
  from zoo.tfpark.tfnet import TFNet
  from zoo.tfpark.tf_dataset import TFNdarrayDataset, check_data_compatible
  from zoo.tfpark.tf_dataset import _standarize_feature_dataset
label_desc: Use image node_id 4 for calling the TFPredictor obj's underlying member method code with example usage: obj.from_keras(cls, keras_model, dataset) and returns: cls