code: def predict(self, input_frame):
      if (
          self.training_generator is None
          or self.validation_generator is None
      ):
          self.prepare_generators()
  
      source_min = 0
  
      if str(input_frame.dtype) == "uint8":
          source_max = 255
      elif str(input_frame.dtype) == "float64":
          source_max = 1
  
      input_frame = np.array(
          serpent.cv.normalize(
              input_frame,
              source_min,
              source_max,
              target_min=-1,
              target_max=1,
          ),
          dtype="float32",
      )
  
      class_mapping = self.training_generator.class_indices
      class_probabilities = self.classifier.predict(
          input_frame[None, :, :, :]
      )[0]
  
      max_probability_index = np.argmax(class_probabilities)
      max_probability = class_probabilities[max_probability_index]
  
      if max_probability < 0.5:
          return None
  
      for class_name, i in class_mapping.items():
          if i == max_probability_index:
              return class_name
  
file_path: SerpentAI/serpent/machine_learning/context_classification/context_classifiers/cnn_inception_v3_context_classifier.py
imports: from serpent.machine_learning.context_classification.context_classifier import ContextClassifier
  from serpent.utilities import SerpentError
  import skimage.transform
  import numpy
  import serpent.cv
label_desc: Use image node_id 4 for calling the CNNInceptionV3ContextClassifier obj's underlying member method code with example usage: obj.predict(input_frame) and returns: None, class_name