class_docstr: Interpreter interface for running TensorFlow Lite models.
  
  Models obtained from `TfLiteConverter` can be run in Python with
  `Interpreter`.
  
  As an example, lets generate a simple Keras model and convert it to TFLite
  (`TfLiteConverter` also supports other input formats with `from_saved_model`
  and `from_concrete_function`)
  
  >>> x = np.array([[1.], [2.]])
  >>> y = np.array([[2.], [4.]])
  >>> model = tf.keras.models.Sequential([
  ...           tf.keras.layers.Dropout(0.2),
  ...           tf.keras.layers.Dense(units=1, input_shape=[1])
  ...         ])
  >>> model.compile(optimizer='sgd', loss='mean_squared_error')
  >>> model.fit(x, y, epochs=1)
  >>> converter = tf.lite.TFLiteConverter.from_keras_model(model)
  >>> tflite_model = converter.convert()
  
  `tflite_model` can be saved to a file and loaded later, or directly into the
  `Interpreter`. Since TensorFlow Lite pre-plans tensor allocations to optimize
  inference, the user needs to call `allocate_tensors()` before any inference.
  
  >>> interpreter = tf.lite.Interpreter(model_content=tflite_model)
  >>> interpreter.allocate_tensors()  # Needed before execution!
  
  Sample execution:
  
  >>> output = interpreter.get_output_details()[0]  # Model has single output.
  >>> input = interpreter.get_input_details()[0]  # Model has single input.
  >>> input_data = tf.constant(1., shape=[1, 1])
  >>> interpreter.set_tensor(input['index'], input_data)
  >>> interpreter.invoke()
  >>> interpreter.get_tensor(output['index']).shape
  (1, 1)
  
  Use `get_signature_runner()` for a more user-friendly inference API.
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docstr: 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.
code: 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()
  
imports: import ctypes
  import enum
  import os
  import platform
  import sys
  import numpy
label_desc: Use image node_id 19 for calling the Interpreter obj's underlying member method code with example usage: obj.invoke() without return types