text: # Lint as: python2, python3
  # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Python TF-Lite interpreter."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import ctypes
  import enum
  import os
  import platform
  import sys
  
  import numpy as np
  
  # pylint: disable=g-import-not-at-top
  if not os.path.splitext(__file__)[0].endswith(
      os.path.join('tflite_runtime', 'interpreter')):
    # This file is part of tensorflow package.
    from tensorflow.lite.python.interpreter_wrapper import _pywrap_tensorflow_interpreter_wrapper as _interpreter_wrapper
    from tensorflow.python.util.tf_export import tf_export as _tf_export
    try:
      from tensorflow.lite.python import metrics_portable as metrics
    except ImportError:
      from tensorflow.lite.python import metrics_nonportable as metrics
  else:
    # This file is part of tflite_runtime package.
    from tflite_runtime import _pywrap_tensorflow_interpreter_wrapper as _interpreter_wrapper
    from tflite_runtime import metrics_portable as metrics
  
    def _tf_export(*x, **kwargs):
      del x, kwargs
      return lambda x: x
  
  
  # pylint: enable=g-import-not-at-top
  
  
  class Delegate(object):
    """Python wrapper class to manage TfLiteDelegate objects.
  
    The shared library is expected to have two functions:
      TfLiteDelegate* tflite_plugin_create_delegate(
          char**, char**, size_t, void (*report_error)(const char *))
      void tflite_plugin_destroy_delegate(TfLiteDelegate*)
  
    The first one creates a delegate object. It may return NULL to indicate an
    error (with a suitable error message reported by calling report_error()).
    The second one destroys delegate object and must be called for every
    created delegate object. Passing NULL as argument value is allowed, i.e.
  
      tflite_plugin_destroy_delegate(tflite_plugin_create_delegate(...))
  
    always works.
    """
  
    def __init__(self, library, options=None):
      """Loads delegate from the shared library.
  
      Args:
        library: Shared library name.
        options: Dictionary of options that are required to load the delegate. All
          keys and values in the dictionary should be serializable. Consult the
          documentation of the specific delegate for required and legal options.
          (default None)
  
      Raises:
        RuntimeError: This is raised if the Python implementation is not CPython.
      """
  
      # TODO(b/136468453): Remove need for __del__ ordering needs of CPython
      # by using explicit closes(). See implementation of Interpreter __del__.
      if platform.python_implementation() != 'CPython':
        raise RuntimeError('Delegates are currently only supported into CPython'
                           'due to missing immediate reference counting.')
  
      self._library = ctypes.pydll.LoadLibrary(library)
      self._library.tflite_plugin_create_delegate.argtypes = [
          ctypes.POINTER(ctypes.c_char_p),
          ctypes.POINTER(ctypes.c_char_p), ctypes.c_int,
          ctypes.CFUNCTYPE(None, ctypes.c_char_p)
      ]
      self._library.tflite_plugin_create_delegate.restype = ctypes.c_void_p
  
      # Convert the options from a dictionary to lists of char pointers.
      options = options or {}
      options_keys = (ctypes.c_char_p * len(options))()
      options_values = (ctypes.c_char_p * len(options))()
      for idx, (key, value) in enumerate(options.items()):
        options_keys[idx] = str(key).encode('utf-8')
        options_values[idx] = str(value).encode('utf-8')
  
      class ErrorMessageCapture(object):
  
        def __init__(self):
          self.message = ''
  
        def report(self, x):
          self.message += x if isinstance(x, str) else x.decode('utf-8')
  
      capture = ErrorMessageCapture()
      error_capturer_cb = ctypes.CFUNCTYPE(None, ctypes.c_char_p)(capture.report)
      # Do not make a copy of _delegate_ptr. It is freed by Delegate's finalizer.
      self._delegate_ptr = self._library.tflite_plugin_create_delegate(
          options_keys, options_values, len(options), error_capturer_cb)
      if self._delegate_ptr is None:
        raise ValueError(capture.message)
  
    def __del__(self):
      # __del__ can not be called multiple times, so if the delegate is destroyed.
      # don't try to destroy it twice.
      if self._library is not None:
        self._library.tflite_plugin_destroy_delegate.argtypes = [ctypes.c_void_p]
        self._library.tflite_plugin_destroy_delegate(self._delegate_ptr)
        self._library = None
  
    def _get_native_delegate_pointer(self):
      """Returns the native TfLiteDelegate pointer.
  
      It is not safe to copy this pointer because it needs to be freed.
  
      Returns:
        TfLiteDelegate *
      """
      return self._delegate_ptr
  
  
  @_tf_export('lite.experimental.load_delegate')
  def load_delegate(library, options=None):
    """Returns loaded Delegate object.
  
    Args:
      library: Name of shared library containing the
        [TfLiteDelegate](https://www.tensorflow.org/lite/performance/delegates).
      options: Dictionary of options that are required to load the delegate. All
        keys and values in the dictionary should be convertible to str. Consult
        the documentation of the specific delegate for required and legal options.
        (default None)
  
    Returns:
      Delegate object.
  
    Raises:
      ValueError: Delegate failed to load.
      RuntimeError: If delegate loading is used on unsupported platform.
    """
    try:
      delegate = Delegate(library, options)
    except ValueError as e:
      raise ValueError('Failed to load delegate from {}\n{}'.format(
          library, str(e)))
    return delegate
  
  
  class SignatureRunner(object):
    """SignatureRunner class for running TFLite models using SignatureDef.
  
    This class should be instantiated through TFLite Interpreter only using
    get_signature_runner method on Interpreter.
    Example,
    signature = interpreter.get_signature_runner("my_signature")
    result = signature(input_1=my_input_1, input_2=my_input_2)
    print(result["my_output"])
    print(result["my_second_output"])
    All names used are this specific SignatureDef names.
  
    Notes:
      No other function on this object or on the interpreter provided should be
      called while this object call has not finished.
    """
  
    def __init__(self, interpreter=None, signature_key=None):
      """Constructor.
  
      Args:
        interpreter: Interpreter object that is already initialized with the
          requested model.
        signature_key: SignatureDef key to be used.
      """
      if not interpreter:
        raise ValueError('None interpreter provided.')
      if not signature_key:
        raise ValueError('None signature_key provided.')
      self._interpreter = interpreter
      self._interpreter_wrapper = interpreter._interpreter
      self._signature_key = signature_key
      signature_defs = interpreter._get_full_signature_list()
      if signature_key not in signature_defs:
        raise ValueError('Invalid signature_key provided.')
      self._signature_def = signature_defs[signature_key]
      self._outputs = self._signature_def['outputs'].items()
      self._inputs = self._signature_def['inputs']
  
      self._subgraph_index = (
          self._interpreter_wrapper.GetSubgraphIndexFromSignature(
              self._signature_key))
  
    def __call__(self, **kwargs):
      """Runs the SignatureDef given the provided inputs in arguments.
  
      Args:
        **kwargs: key,value for inputs to the model. Key is the SignatureDef input
          name. Value is numpy array with the value.
  
      Returns:
        dictionary of the results from the model invoke.
        Key in the dictionary is SignatureDef output name.
        Value is the result Tensor.
      """
  
      if len(kwargs) != len(self._inputs):
        raise ValueError(
            'Invalid number of inputs provided for running a SignatureDef, '
            'expected %s vs provided %s' % (len(self._inputs), len(kwargs)))
  
      # Resize input tensors
      for input_name, value in kwargs.items():
        if input_name not in self._inputs:
          raise ValueError('Invalid Input name (%s) for SignatureDef' %
                           input_name)
        self._interpreter_wrapper.ResizeInputTensor(
            self._inputs[input_name], np.array(value.shape, dtype=np.int32),
            False, self._subgraph_index)
      # Allocate tensors.
      self._interpreter_wrapper.AllocateTensors(self._subgraph_index)
      # Set the input values.
      for input_name, value in kwargs.items():
        self._interpreter_wrapper.SetTensor(self._inputs[input_name], value,
                                            self._subgraph_index)
  
      self._interpreter_wrapper.Invoke(self._subgraph_index)
      result = {}
      for output_name, output_index in self._outputs:
        result[output_name] = self._interpreter_wrapper.GetTensor(
            output_index, self._subgraph_index)
      return result
  
  
  @_tf_export('lite.experimental.OpResolverType')
  @enum.unique
  class OpResolverType(enum.Enum):
    """Different types of op resolvers for Tensorflow Lite.
  
    * `AUTO`: Indicates the op resolver that is chosen by default in TfLite
       Python, which is the "BUILTIN" as described below.
    * `BUILTIN`: Indicates the op resolver for built-in ops with optimized kernel
      implementation.
    * `BUILTIN_REF`: Indicates the op resolver for built-in ops with reference
      kernel implementation. It's generally used for testing and debugging.
    * `BUILTIN_WITHOUT_DEFAULT_DELEGATES`: Indicates the op resolver for
      built-in ops with optimized kernel implementation, but it will disable
      the application of default TfLite delegates (like the XNNPACK delegate) to
      the model graph. Generally this should not be used unless there are issues
      with the default configuration.
    """
    # Corresponds to an op resolver chosen by default in TfLite Python.
    AUTO = 0
  
    # Corresponds to tflite::ops::builtin::BuiltinOpResolver in C++.
    BUILTIN = 1
  
    # Corresponds to tflite::ops::builtin::BuiltinRefOpResolver in C++.
    BUILTIN_REF = 2
  
    # Corresponds to
    # tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates in C++.
    BUILTIN_WITHOUT_DEFAULT_DELEGATES = 3
  
  
  def _get_op_resolver_id(op_resolver_type=OpResolverType.AUTO):
    """Get a integer identifier for the op resolver."""
  
    # Note: the integer identifier value needs to be same w/ op resolver ids
    # defined in interpreter_wrapper/interpreter_wrapper.cc.
    return {
        # Note AUTO and BUILTIN currently share the same identifier.
        OpResolverType.AUTO: 1,
        OpResolverType.BUILTIN: 1,
        OpResolverType.BUILTIN_REF: 2,
        OpResolverType.BUILTIN_WITHOUT_DEFAULT_DELEGATES: 3
    }.get(op_resolver_type, None)
  
  
  @_tf_export('lite.Interpreter')
  class Interpreter(object):
    """Interpreter interface for TensorFlow Lite Models.
  
    This makes the TensorFlow Lite interpreter accessible in Python.
    It is possible to use this interpreter in a multithreaded Python environment,
    but you must be sure to call functions of a particular instance from only
    one thread at a time. So if you want to have 4 threads running different
    inferences simultaneously, create  an interpreter for each one as thread-local
    data. Similarly, if you are calling invoke() in one thread on a single
    interpreter but you want to use tensor() on another thread once it is done,
    you must use a synchronization primitive between the threads to ensure invoke
    has returned before calling tensor().
    """
  
    def __init__(self,
                 model_path=None,
                 model_content=None,
                 experimental_delegates=None,
                 num_threads=None,
                 experimental_op_resolver_type=OpResolverType.AUTO,
                 experimental_preserve_all_tensors=False):
      """Constructor.
  
      Args:
        model_path: Path to TF-Lite Flatbuffer file.
        model_content: Content of model.
        experimental_delegates: Experimental. Subject to change. List of
          [TfLiteDelegate](https://www.tensorflow.org/lite/performance/delegates)
            objects returned by lite.load_delegate().
        num_threads: Sets the number of threads used by the interpreter and
          available to CPU kernels. If not set, the interpreter will use an
          implementation-dependent default number of threads. Currently, only a
          subset of kernels, such as conv, support multi-threading. num_threads
          should be >= -1. Setting num_threads to 0 has the effect to disable
          multithreading, which is equivalent to setting num_threads to 1. If set
          to the value -1, the number of threads used will be
          implementation-defined and platform-dependent.
        experimental_op_resolver_type: The op resolver used by the interpreter. It
          must be an instance of OpResolverType. By default, we use the built-in
          op resolver which corresponds to tflite::ops::builtin::BuiltinOpResolver
          in C++.
        experimental_preserve_all_tensors: If true, then intermediate tensors used
          during computation are preserved for inspection, and if the passed op
          resolver type is AUTO or BUILTIN, the type will be changed to
          BUILTIN_WITHOUT_DEFAULT_DELEGATES so that no Tensorflow Lite default
          delegates are applied. If false, getting intermediate tensors could
          result in undefined values or None, especially when the graph is
          successfully modified by the Tensorflow Lite default delegate.
  
      Raises:
        ValueError: If the interpreter was unable to create.
      """
      if not hasattr(self, '_custom_op_registerers'):
        self._custom_op_registerers = []
  
      actual_resolver_type = experimental_op_resolver_type
      if experimental_preserve_all_tensors and (
          experimental_op_resolver_type == OpResolverType.AUTO or
          experimental_op_resolver_type == OpResolverType.BUILTIN):
        actual_resolver_type = OpResolverType.BUILTIN_WITHOUT_DEFAULT_DELEGATES
      op_resolver_id = _get_op_resolver_id(actual_resolver_type)
      if op_resolver_id is None:
        raise ValueError('Unrecognized passed in op resolver type: {}'.format(
            experimental_op_resolver_type))
  
      if model_path and not model_content:
        custom_op_registerers_by_name = [
            x for x in self._custom_op_registerers if isinstance(x, str)
        ]
        custom_op_registerers_by_func = [
            x for x in self._custom_op_registerers if not isinstance(x, str)
        ]
        self._interpreter = (
            _interpreter_wrapper.CreateWrapperFromFile(
                model_path, op_resolver_id, custom_op_registerers_by_name,
                custom_op_registerers_by_func, experimental_preserve_all_tensors))
        if not self._interpreter:
          raise ValueError('Failed to open {}'.format(model_path))
      elif model_content and not model_path:
        custom_op_registerers_by_name = [
            x for x in self._custom_op_registerers if isinstance(x, str)
        ]
        custom_op_registerers_by_func = [
            x for x in self._custom_op_registerers if not isinstance(x, str)
        ]
        # Take a reference, so the pointer remains valid.
        # Since python strings are immutable then PyString_XX functions
        # will always return the same pointer.
        self._model_content = model_content
        self._interpreter = (
            _interpreter_wrapper.CreateWrapperFromBuffer(
                model_content, op_resolver_id, custom_op_registerers_by_name,
                custom_op_registerers_by_func, experimental_preserve_all_tensors))
      elif not model_content and not model_path:
        raise ValueError('`model_path` or `model_content` must be specified.')
      else:
        raise ValueError('Can\'t both provide `model_path` and `model_content`')
  
      if num_threads is not None:
        if not isinstance(num_threads, int):
          raise ValueError('type of num_threads should be int')
        if num_threads < 1:
          raise ValueError('num_threads should >= 1')
        self._interpreter.SetNumThreads(num_threads)
  
      # Each delegate is a wrapper that owns the delegates that have been loaded
      # as plugins. The interpreter wrapper will be using them, but we need to
      # hold them in a list so that the lifetime is preserved at least as long as
      # the interpreter wrapper.
      self._delegates = []
      if experimental_delegates:
        self._delegates = experimental_delegates
        for delegate in self._delegates:
          self._interpreter.ModifyGraphWithDelegate(
              delegate._get_native_delegate_pointer())  # pylint: disable=protected-access
      self._signature_defs = self.get_signature_list()
  
      self._metrics = metrics.TFLiteMetrics()
      self._metrics.increase_counter_interpreter_creation()
  
    def __del__(self):
      # Must make sure the interpreter is destroyed before things that
      # are used by it like the delegates. NOTE this only works on CPython
      # probably.
      # TODO(b/136468453): Remove need for __del__ ordering needs of CPython
      # by using explicit closes(). See implementation of Interpreter __del__.
      self._interpreter = None
      self._delegates = None
  
    def allocate_tensors(self):
      self._ensure_safe()
      return self._interpreter.AllocateTensors()
  
    def _safe_to_run(self):
      """Returns true if there exist no numpy array buffers.
  
      This means it is safe to run tflite calls that may destroy internally
      allocated memory. This works, because in the wrapper.cc we have made
      the numpy base be the self._interpreter.
      """
      # NOTE, our tensor() call in cpp will use _interpreter as a base pointer.
      # If this environment is the only _interpreter, then the ref count should be
      # 2 (1 in self and 1 in temporary of sys.getrefcount).
      return sys.getrefcount(self._interpreter) == 2
  
    def _ensure_safe(self):
      """Makes sure no numpy arrays pointing to internal buffers are active.
  
      This should be called from any function that will call a function on
      _interpreter that may reallocate memory e.g. invoke(), ...
  
      Raises:
        RuntimeError: If there exist numpy objects pointing to internal memory
          then we throw.
      """
      if not self._safe_to_run():
        raise RuntimeError("""There is at least 1 reference to internal data
        in the interpreter in the form of a numpy array or slice. Be sure to
        only hold the function returned from tensor() if you are using raw
        data access.""")
  
    # Experimental and subject to change
    def _get_op_details(self, op_index):
      """Gets a dictionary with arrays of ids for tensors involved with an op.
  
      Args:
        op_index: Operation/node index of node to query.
  
      Returns:
        a dictionary containing the index, op name, and arrays with lists of the
        indices for the inputs and outputs of the op/node.
      """
      op_index = int(op_index)
      op_name = self._interpreter.NodeName(op_index)
      op_inputs = self._interpreter.NodeInputs(op_index)
      op_outputs = self._interpreter.NodeOutputs(op_index)
  
      details = {
          'index': op_index,
          'op_name': op_name,
          'inputs': op_inputs,
          'outputs': op_outputs,
      }
  
      return details
  
    def _get_tensor_details(self, tensor_index):
      """Gets tensor details.
  
      Args:
        tensor_index: Tensor index of tensor to query.
  
      Returns:
        A dictionary containing the following fields of the tensor:
          'name': The tensor name.
          'index': The tensor index in the interpreter.
          'shape': The shape of the tensor.
          'quantization': Deprecated, use 'quantization_parameters'. This field
              only works for per-tensor quantization, whereas
              'quantization_parameters' works in all cases.
          'quantization_parameters': The parameters used to quantize the tensor:
            'scales': List of scales (one if per-tensor quantization)
            'zero_points': List of zero_points (one if per-tensor quantization)
            'quantized_dimension': Specifies the dimension of per-axis
                quantization, in the case of multiple scales/zero_points.
  
      Raises:
        ValueError: If tensor_index is invalid.
      """
      tensor_index = int(tensor_index)
      tensor_name = self._interpreter.TensorName(tensor_index)
      tensor_size = self._interpreter.TensorSize(tensor_index)
      tensor_size_signature = self._interpreter.TensorSizeSignature(tensor_index)
      tensor_type = self._interpreter.TensorType(tensor_index)
      tensor_quantization = self._interpreter.TensorQuantization(tensor_index)
      tensor_quantization_params = self._interpreter.TensorQuantizationParameters(
          tensor_index)
      tensor_sparsity_params = self._interpreter.TensorSparsityParameters(
          tensor_index)
  
      if not tensor_type:
        raise ValueError('Could not get tensor details')
  
      details = {
          'name': tensor_name,
          'index': tensor_index,
          'shape': tensor_size,
          'shape_signature': tensor_size_signature,
          'dtype': tensor_type,
          'quantization': tensor_quantization,
          'quantization_parameters': {
              'scales': tensor_quantization_params[0],
              'zero_points': tensor_quantization_params[1],
              'quantized_dimension': tensor_quantization_params[2],
          },
          'sparsity_parameters': tensor_sparsity_params
      }
  
      return details
  
    # Experimental and subject to change
    def _get_ops_details(self):
      """Gets op details for every node.
  
      Returns:
        A list of dictionaries containing arrays with lists of tensor ids for
        tensors involved in the op.
      """
      return [
          self._get_op_details(idx) for idx in range(self._interpreter.NumNodes())
      ]
  
    def get_tensor_details(self):
      """Gets tensor details for every tensor with valid tensor details.
  
      Tensors where required information about the tensor is not found are not
      added to the list. This includes temporary tensors without a name.
  
      Returns:
        A list of dictionaries containing tensor information.
      """
      tensor_details = []
      for idx in range(self._interpreter.NumTensors()):
        try:
          tensor_details.append(self._get_tensor_details(idx))
        except ValueError:
          pass
      return tensor_details
  
    def get_input_details(self):
      """Gets model input tensor details.
  
      Returns:
        A list in which each item is a dictionary with details about
        an input tensor. Each dictionary contains the following fields
        that describe the tensor:
  
        + `name`: The tensor name.
        + `index`: The tensor index in the interpreter.
        + `shape`: The shape of the tensor.
        + `shape_signature`: Same as `shape` for models with known/fixed shapes.
          If any dimension sizes are unkown, they are indicated with `-1`.
        + `dtype`: The numpy data type (such as `np.int32` or `np.uint8`).
        + `quantization`: Deprecated, use `quantization_parameters`. This field
          only works for per-tensor quantization, whereas
          `quantization_parameters` works in all cases.
        + `quantization_parameters`: A dictionary of parameters used to quantize
          the tensor:
          ~ `scales`: List of scales (one if per-tensor quantization).
          ~ `zero_points`: List of zero_points (one if per-tensor quantization).
          ~ `quantized_dimension`: Specifies the dimension of per-axis
          quantization, in the case of multiple scales/zero_points.
        + `sparsity_parameters`: A dictionary of parameters used to encode a
          sparse tensor. This is empty if the tensor is dense.
      """
      return [
          self._get_tensor_details(i) for i in self._interpreter.InputIndices()
      ]
  
    def set_tensor(self, tensor_index, value):
      """Sets the value of the input tensor.
  
      Note this copies data in `value`.
  
      If you want to avoid copying, you can use the `tensor()` function to get a
      numpy buffer pointing to the input buffer in the tflite interpreter.
  
      Args:
        tensor_index: Tensor index of tensor to set. This value can be gotten from
          the 'index' field in get_input_details.
        value: Value of tensor to set.
  
      Raises:
        ValueError: If the interpreter could not set the tensor.
      """
      self._interpreter.SetTensor(tensor_index, value)
  
    def resize_tensor_input(self, input_index, tensor_size, strict=False):
      """Resizes an input tensor.
  
      Args:
        input_index: Tensor index of input to set. This value can be gotten from
          the 'index' field in get_input_details.
        tensor_size: The tensor_shape to resize the input to.
        strict: Only unknown dimensions can be resized when `strict` is True.
          Unknown dimensions are indicated as `-1` in the `shape_signature`
          attribute of a given tensor. (default False)
  
      Raises:
        ValueError: If the interpreter could not resize the input tensor.
  
      Usage:
      ```
      interpreter = Interpreter(model_content=tflite_model)
      interpreter.resize_tensor_input(0, [num_test_images, 224, 224, 3])
      interpreter.allocate_tensors()
      interpreter.set_tensor(0, test_images)
      interpreter.invoke()
      ```
      """
      self._ensure_safe()
      # `ResizeInputTensor` now only accepts int32 numpy array as `tensor_size
      # parameter.
      tensor_size = np.array(tensor_size, dtype=np.int32)
      self._interpreter.ResizeInputTensor(input_index, tensor_size, strict)
  
    def get_output_details(self):
      """Gets model output tensor details.
  
      Returns:
        A list in which each item is a dictionary with details about
        an output tensor. The dictionary contains the same fields as
        described for `get_input_details()`.
      """
      return [
          self._get_tensor_details(i) for i in self._interpreter.OutputIndices()
      ]
  
    def get_signature_list(self):
      """Gets list of SignatureDefs in the model.
  
      Example,
      ```
      signatures = interpreter.get_signature_list()
      print(signatures)
  
      # {
      #   'add': {'inputs': ['x', 'y'], 'outputs': ['output_0']}
      # }
  
      Then using the names in the signature list you can get a callable from
      get_signature_runner().
      ```
  
      Returns:
        A list of SignatureDef details in a dictionary structure.
        It is keyed on the SignatureDef method name, and the value holds
        dictionary of inputs and outputs.
      """
      full_signature_defs = self._interpreter.GetSignatureDefs()
      for _, signature_def in full_signature_defs.items():
        signature_def['inputs'] = list(signature_def['inputs'].keys())
        signature_def['outputs'] = list(signature_def['outputs'].keys())
      return full_signature_defs
  
    def _get_full_signature_list(self):
      """Gets list of SignatureDefs in the model.
  
      Example,
      ```
      signatures = interpreter._get_full_signature_list()
      print(signatures)
  
      # {
      #   'add': {'inputs': {'x': 1, 'y': 0}, 'outputs': {'output_0': 4}}
      # }
  
      Then using the names in the signature list you can get a callable from
      get_signature_runner().
      ```
  
      Returns:
        A list of SignatureDef details in a dictionary structure.
        It is keyed on the SignatureDef method name, and the value holds
        dictionary of inputs and outputs.
      """
      return self._interpreter.GetSignatureDefs()
  
    def _set_input_tensor(self, input_name, value, signature_key=None):
      """Sets the value of the input tensor.
  
      Input tensor is identified by `input_name` in the SignatureDef identified
      by `signature_key`.
      If the model has a single SignatureDef then you can pass None as
      `signature_key`.
  
      Note this copies data in `value`.
  
      Example,
      ```
      input_data = np.array([1.2, 1.4], np.float32)
      signatures = interpreter._get_full_signature_list()
      print(signatures)
      # {
      #   'add': {'inputs': {'x': 1, 'y': 0}, 'outputs': {'output_0': 4}}
      # }
      interpreter._set_input_tensor(input_name='x', value=input_data,
      signature_key='add_fn')
      ```
  
      Args:
        input_name: Name of the output tensor in the SignatureDef.
        value: Value of tensor to set as a numpy array.
        signature_key: The signature key for the SignatureDef, it can be None
          if and only if the model has a single SignatureDef. Default value is
          None.
  
      Raises:
        ValueError: If the interpreter could not set the tensor. Or
        if `signature_key` is None and model doesn't have a single
        Signature.
      """
      if signature_key is None:
        if len(self._signature_defs) != 1:
          raise ValueError(
              'SignatureDef signature_key is None and model has {0} Signatures. '
              'None is only allowed when the model has 1 SignatureDef'.format(
                  len(self._signature_defs)))
        else:
          signature_key = next(iter(self._signature_defs))
      self._interpreter.SetInputTensorFromSignatureDefName(
          input_name, signature_key, value)
  
    def get_signature_runner(self, signature_key=None):
      """Gets callable for inference of specific SignatureDef.
  
      Example usage,
      ```
      interpreter = tf.lite.Interpreter(model_content=tflite_model)
      interpreter.allocate_tensors()
      fn = interpreter.get_signature_runner('div_with_remainder')
      output = fn(x=np.array([3]), y=np.array([2]))
      print(output)
      # {
      #   'quotient': array([1.], dtype=float32)
      #   'remainder': array([1.], dtype=float32)
      # }
      ```
  
      None can be passed for signature_key if the model has a single Signature
      only.
  
      All names used are this specific SignatureDef names.
  
  
      Args:
        signature_key: Signature key for the SignatureDef, it can be None if and
          only if the model has a single SignatureDef. Default value is None.
  
      Returns:
        This returns a callable that can run inference for SignatureDef defined
        by argument 'signature_key'.
        The callable will take key arguments corresponding to the arguments of the
        SignatureDef, that should have numpy values.
        The callable will returns dictionary that maps from output names to numpy
        values of the computed results.
  
      Raises:
        ValueError: If passed signature_key is invalid.
      """
      if signature_key is None:
        if len(self._signature_defs) != 1:
          raise ValueError(
              'SignatureDef signature_key is None and model has {0} Signatures. '
              'None is only allowed when the model has 1 SignatureDef'.format(
                  len(self._signature_defs)))
        else:
          signature_key = next(iter(self._signature_defs))
      return SignatureRunner(interpreter=self, signature_key=signature_key)
  
    def get_tensor(self, tensor_index):
      """Gets the value of the output tensor (get a copy).
  
      If you wish to avoid the copy, use `tensor()`. This function cannot be used
      to read intermediate results.
  
      Args:
        tensor_index: Tensor index of tensor to get. This value can be gotten from
          the 'index' field in get_output_details.
  
      Returns:
        a numpy array.
      """
      return self._interpreter.GetTensor(tensor_index)
  
    def tensor(self, tensor_index):
      """Returns function that gives a numpy view of the current tensor buffer.
  
      This allows reading and writing to this tensors w/o copies. This more
      closely mirrors the C++ Interpreter class interface's tensor() member, hence
      the name. Be careful to not hold these output references through calls
      to `allocate_tensors()` and `invoke()`. This function cannot be used to read
      intermediate results.
  
      Usage:
  
      ```
      interpreter.allocate_tensors()
      input = interpreter.tensor(interpreter.get_input_details()[0]["index"])
      output = interpreter.tensor(interpreter.get_output_details()[0]["index"])
      for i in range(10):
        input().fill(3.)
        interpreter.invoke()
        print("inference %s" % output())
      ```
  
      Notice how this function avoids making a numpy array directly. This is
      because it is important to not hold actual numpy views to the data longer
      than necessary. If you do, then the interpreter can no longer be invoked,
      because it is possible the interpreter would resize and invalidate the
      referenced tensors. The NumPy API doesn't allow any mutability of the
      the underlying buffers.
  
      WRONG:
  
      ```
      input = interpreter.tensor(interpreter.get_input_details()[0]["index"])()
      output = interpreter.tensor(interpreter.get_output_details()[0]["index"])()
      interpreter.allocate_tensors()  # This will throw RuntimeError
      for i in range(10):
        input.fill(3.)
        interpreter.invoke()  # this will throw RuntimeError since input,output
      ```
  
      Args:
        tensor_index: Tensor index of tensor to get. This value can be gotten from
          the 'index' field in get_output_details.
  
      Returns:
        A function that can return a new numpy array pointing to the internal
        TFLite tensor state at any point. It is safe to hold the function forever,
        but it is not safe to hold the numpy array forever.
      """
      return lambda: self._interpreter.tensor(self._interpreter, tensor_index)
  
    def invoke(self):
      """Invoke the interpreter.
  
      Be sure to set the input sizes, allocate tensors and fill values before
      calling this. Also, note that this function releases the GIL so heavy
      computation can be done in the background while the Python interpreter
      continues. No other function on this object should be called while the
      invoke() call has not finished.
  
      Raises:
        ValueError: When the underlying interpreter fails raise ValueError.
      """
      self._ensure_safe()
      self._interpreter.Invoke()
  
    def reset_all_variables(self):
      return self._interpreter.ResetVariableTensors()
  
    # Experimental and subject to change.
    def _native_handle(self):
      """Returns a pointer to the underlying tflite::Interpreter instance.
  
      This allows extending tflite.Interpreter's functionality in a custom C++
      function. Consider how that may work in a custom pybind wrapper:
  
        m.def("SomeNewFeature", ([](py::object handle) {
          auto* interpreter =
            reinterpret_cast<tflite::Interpreter*>(handle.cast<intptr_t>());
          ...
        }))
  
      and corresponding Python call:
  
        SomeNewFeature(interpreter.native_handle())
  
      Note: This approach is fragile. Users must guarantee the C++ extension build
      is consistent with the tflite.Interpreter's underlying C++ build.
      """
      return self._interpreter.interpreter()
  
  
  class InterpreterWithCustomOps(Interpreter):
    """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.
    """
  
    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)
  