class_docstr: Model optimized using ApacheTVM with a tensorflow interface.
  
  This class can be used exactly in the same way as a tf.Module or
  keras.Model object.
  At prediction time it takes as input tensorflow tensors given as positional
  arguments.
  
  Attributes:
      network_parameters (ModelParams): The model parameters as batch
              size, input and output sizes.
      exec_network (any): The graph executor. This is the
          central component in the OpenVino optimized model execution.
      input_keys (List): Keys associated to the inputs.
      output_keys (List): Keys associated to the outputs.
      description_file (str): File containing a description of the optimized
          model.
      weights_file (str): File containing the model weights.
docstr: Predict on the input tensors.
  
  Note that the input tensors must be on the same batch. If a sequence
  of tensors is given when the model is expecting a single input tensor
  (with batch size >= 1) an error is raised.
  
  Args:
      input_tensors (Tuple[Tensor]): Input tensors belonging to the same
          batch. The tensors are expected having dimensions
          (batch_size, dim1, dim2, ...).
  
  Returns:
      Tuple[Tensor]: Output tensors. Note that the output tensors does
          not correspond to the prediction on the input tensors with a
          1 to 1 mapping. In fact the output tensors are produced as the
          multiple-output of the model given a (multi-) tensor input.
code: def run(self, *input_tensors: tf.Tensor) -> Tuple[tf.Tensor, ...]:
      """Predict on the input tensors.
  
      Note that the input tensors must be on the same batch. If a sequence
      of tensors is given when the model is expecting a single input tensor
      (with batch size >= 1) an error is raised.
  
      Args:
          input_tensors (Tuple[Tensor]): Input tensors belonging to the same
              batch. The tensors are expected having dimensions
              (batch_size, dim1, dim2, ...).
  
      Returns:
          Tuple[Tensor]: Output tensors. Note that the output tensors does
              not correspond to the prediction on the input tensors with a
              1 to 1 mapping. In fact the output tensors are produced as the
              multiple-output of the model given a (multi-) tensor input.
      """
      input_arrays = (
          input_tensor.numpy() for input_tensor in input_tensors
      )
      output_arrays = self._predict_array(input_arrays)
      # noinspection PyTypeChecker
      return tuple(
          tf.convert_to_tensor(output_array)
          for output_array in output_arrays
      )
  
imports: import json
  import shutil
  from abc import ABC
  from pathlib import Path
  from typing import Dict, Union, Type, Generator, Tuple, List, Optional
  import numpy
  from loguru import logger
  from nebullvm.config import OPENVINO_FILENAMES
  from nebullvm.core.models import Device, ModelParams, DeepLearningFramework
  from nebullvm.operations.inference_learners.base import BaseInferenceLearner, LearnerMetadata, PytorchBaseInferenceLearner, TensorflowBaseInferenceLearner, NumpyBaseInferenceLearner
  from nebullvm.optional_modules.openvino import Core, Model, CompiledModel, InferRequest
  from nebullvm.optional_modules.tensorflow import tensorflow
  from nebullvm.optional_modules.torch import torch
  from nebullvm.tools.data import DataManager
  from nebullvm.tools.transformations import MultiStageTransformation
label_desc: Use image node_id 1 for calling the TensorflowOpenVinoInferenceLearner obj's underlying member method code with example usage: obj.run() and returns: tuple