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: 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.
    subgraph_index: Index of the subgraph to fetch the tensor. Default value
      is 0, which means to fetch from the primary subgraph.
  
  Returns:
    a numpy array.
code: def get_tensor(self, tensor_index, subgraph_index=0):
      """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.
        subgraph_index: Index of the subgraph to fetch the tensor. Default value
          is 0, which means to fetch from the primary subgraph.
  
      Returns:
        a numpy array.
      """
      return self._interpreter.GetTensor(tensor_index, subgraph_index)
  
imports: import ctypes
  import enum
  import os
  import platform
  import sys
  import numpy
label_desc: Use image node_id 17 for calling the Interpreter obj's underlying member method code with example usage: obj.get_tensor(tensor_index, subgraph_index) and returns: self