class_docstr: Abstract class for TensorFlow model loader.
class_docstr_tok: ["Abstract","class","for","TensorFlow","model","loader","."]
docstr: TensorFlow model loader.
  
  Parameters
  ----------
  model: TensorFlow model
      Model generated using TensorFlow.
  debug: bool, optional, defaults to False
      If true, display verbose logging and visualizations.
  kwargs: dict(str, Any), optional, defaults to None
      Dictionary of additional arguments.
docstr_tok: ["TensorFlow","model","loader",".","Parameters","--","--","--","--","--","model",":","TensorFlow","model","Model","generated","using","TensorFlow",".","debug",":","bool",",","optional",",","defaults","to","False","If","true",",","display","verbose","logging","and","visualizations",".","kwargs",":","dict","(","str",",","Any",")",",","optional",",","defaults","to","None","Dictionary","of","additional","arguments","."]
code: def __init__(self, model, debug=False, **kwargs):
      """
      TensorFlow model loader.
  
      Parameters
      ----------
      model: TensorFlow model
          Model generated using TensorFlow.
      debug: bool, optional, defaults to False
          If true, display verbose logging and visualizations.
      kwargs: dict(str, Any), optional, defaults to None
          Dictionary of additional arguments.
      """
      self.model = model
      self.debug = debug
      self.kwargs = kwargs
      self._graph_def = None
      self._tf_ssa = None
  
imports: import gc
  import os
  from distutils.version import StrictVersion
  from tempfile import mktemp
  import tensorflow
  from tqdm import tqdm
  from coremltools import _logger
  from coremltools._deps import _get_version
  from coremltools.converters._profile_utils import _profile
  from .._utils import get_output_names
  from .basic_graph_ops import fill_outputs
  from .converter import TFConverter
  from .parsed_tf_node import ParsedTFNode
  from .tf_graph_pass import cond_to_where, constant_propagation, delete_asserts, delete_disconnected_nodes, delete_unnecessary_constant_nodes, functionalize_loops, fuse_dilation_conv, insert_get_tuple, quantization_pass, remove_variable_nodes, tensor_array_resource_removal
  from .tfssa import NetworkEnsemble, SSAFunction
label_desc: Use image node_id 1 to create a new TFLoader object  with example: obj = TFLoader(model, debug)