class_docstr: Interpreter interface for TensorFlow Lite Models that accepts custom ops.
  
  The interface provided by this class is experimental and therefore not exposed
  as part of the public API.
  
  Wraps the tf.lite.Interpreter class and adds the ability to load custom ops
  by providing the names of functions that take a pointer to a BuiltinOpResolver
  and add a custom op.
class_docstr_tok: ["Interpreter","interface","for","TensorFlow","Lite","Models","that","accepts","custom","ops",".","The","interface","provided","by","this","class","is","experimental","and","therefore","not","exposed","as","part","of","the","public","API",".","Wraps","the","tf.lite.Interpreter","class","and","adds","the","ability","to","load","custom","ops","by","providing","the","names","of","functions","that","take","a","pointer","to","a","BuiltinOpResolver","and","add","a","custom","op","."]
docstr: Constructor.
  
  Args:
    custom_op_registerers: List of str (symbol names) or functions that take a
      pointer to a MutableOpResolver and register a custom op. When passing
      functions, use a pybind function that takes a uintptr_t that can be
      recast as a pointer to a MutableOpResolver.
    **kwargs: Additional arguments passed to Interpreter.
  
  Raises:
    ValueError: If the interpreter was unable to create.
code: def __init__(self, custom_op_registerers=None, **kwargs):
      """Constructor.
  
      Args:
        custom_op_registerers: List of str (symbol names) or functions that take a
          pointer to a MutableOpResolver and register a custom op. When passing
          functions, use a pybind function that takes a uintptr_t that can be
          recast as a pointer to a MutableOpResolver.
        **kwargs: Additional arguments passed to Interpreter.
  
      Raises:
        ValueError: If the interpreter was unable to create.
      """
      self._custom_op_registerers = custom_op_registerers or []
      super(InterpreterWithCustomOps, self).__init__(**kwargs)
  
imports: import ctypes
  import enum
  import os
  import platform
  import sys
  import numpy
label_desc: Use image node_id 1 to create a new InterpreterWithCustomOps object from inherited base classes: Interpreter with example: obj = InterpreterWithCustomOps(custom_op_registerers)