docstr: TensorFlow 2.x model loader.
  
  Parameters
  ----------
  model: Model created with TensorFlow 2.x
      One of the following model format:
          - TensorFlow tf.keras.Model object or HDF5 (.h5) file path
          - TensorFlow SavedModel directory path
          - TensorFlow list of concrete functions(s)
  debug: bool, optional. Defaults to False.
      This flag should generally be False except for debugging purposes
      for diagnosing conversion errors. Setting this flag to True will
      cause graph pass errors to be ignored, forcefully returning a
      NetworkEnsemble object.
  kwargs: dict(str, Any), optional
      Dictionary of additional arguments.
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code: def __init__(self, model, debug=False, **kwargs):
      """
      TensorFlow 2.x model loader.
  
      Parameters
      ----------
      model: Model created with TensorFlow 2.x
          One of the following model format:
              - TensorFlow tf.keras.Model object or HDF5 (.h5) file path
              - TensorFlow SavedModel directory path
              - TensorFlow list of concrete functions(s)
      debug: bool, optional. Defaults to False.
          This flag should generally be False except for debugging purposes
          for diagnosing conversion errors. Setting this flag to True will
          cause graph pass errors to be ignored, forcefully returning a
          NetworkEnsemble object.
      kwargs: dict(str, Any), optional
          Dictionary of additional arguments.
      """
      TFLoader.__init__(self, model, debug, **kwargs)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import logging
  import os.path
  from six import string_types
  from tqdm import tqdm
  import tensorflow
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
  from tensorflow.python.framework.function_def_to_graph import function_def_to_graph
  from tensorflow.python.keras.saving import saving_utils
  from tensorflow.lite.python.util import run_graph_optimizations
  from tensorflow.lite.python.util import get_grappler_config
  from .converter import TF2Converter
  from coremltools.converters.mil.frontend.tensorflow.basic_graph_ops import fill_outputs
  from coremltools.converters.mil.frontend.tensorflow.tf_graph_pass import constant_propagation, remove_variable_nodes, tensor_array_resource_removal, insert_get_tuple, delete_disconnected_nodes, fuse_dilation_conv
  from coremltools.converters.mil.frontend.tensorflow2.tf_graph_pass import flatten_sub_graph_namespaces, rewrite_control_flow_functions
  from coremltools.converters.mil.frontend.tensorflow.tfssa import NetworkEnsemble, SSAFunction
  from coremltools.converters.mil.frontend.tensorflow.parsed_tf_node import ParsedTFNode
  from coremltools.converters.mil.frontend.tensorflow.load import TFLoader
label_desc: Use image node_id 1 to create a new TF2Loader object from inherited base classes: TFLoader with example: obj = TF2Loader(model, debug)