class_docstr: Debugger for Quantized TensorFlow Lite debug mode models.
  
  This can run the TensorFlow Lite converted models equipped with debug ops and
  collect debug information. This debugger calculates statistics from
  user-defined post-processing functions as well as default ones.
docstr: Sets input tensors into TFLite model Interpreter.
  
  Args:
    interpreter: a tf.lite.Interpreter object with allocated tensors.
    tensor_data: a list of Numpy array data.
    initialize: set to true when input is first set for the interpreter, to
      set input shapes and allocate tensors.
  
  Raises:
    ValueError: when inputs can't be set, or size of provided inputs does not
    match size of model inputs.
docstr_tok: ["Sets","input","tensors","into","TFLite","model","Interpreter",".","Args",":","interpreter",":","a","tf.lite.Interpreter","object","with","allocated","tensors",".","tensor_data",":","a","list","of","Numpy","array","data",".","initialize",":","set","to","true","when","input","is","first","set","for","the","interpreter",",","to","set","input","shapes","and","allocate","tensors",".","Raises",":","ValueError",":","when","inputs","ca","n't","be","set",",","or","size","of","provided","inputs","does","not","match","size","of","model","inputs","."]
code: def _set_input_tensors(
      self,
      interpreter: _interpreter.Interpreter,
      tensor_data: Sequence[np.ndarray],
      initialize: bool,
  ) -> None:
      """Sets input tensors into TFLite model Interpreter.
  
      Args:
        interpreter: a tf.lite.Interpreter object with allocated tensors.
        tensor_data: a list of Numpy array data.
        initialize: set to true when input is first set for the interpreter, to
          set input shapes and allocate tensors.
  
      Raises:
        ValueError: when inputs can't be set, or size of provided inputs does not
        match size of model inputs.
      """
      input_details = interpreter.get_input_details()
      if len(input_details) != len(tensor_data):
          raise ValueError(
              "Number of inputs provided ({}) does not match number of inputs to "
              "the model ({})".format(
                  len(tensor_data), len(input_details)
              )
          )
  
      if initialize:
          for input_detail, tensor in zip(input_details, tensor_data):
              interpreter.resize_tensor_input(
                  input_detail["index"], tensor.shape
              )
          interpreter.allocate_tensors()
  
      for input_detail, tensor in zip(input_details, tensor_data):
          if (
              tensor.dtype == np.float32
              and input_detail["dtype"] == np.int8
          ):
              quant_params = _get_quant_params(input_detail)
              if quant_params:
                  scale, zero_point = quant_params
                  tensor = np.round(
                      (tensor / scale) + zero_point
                  ).astype(np.int8)
          interpreter.set_tensor(input_detail["index"], tensor)
  
imports: import collections
  import csv
  import re
  from typing import Any, Callable, Dict, IO, Iterable, List, Mapping, Optional, Sequence, Tuple
  import numpy
  from tensorflow.lite.python import convert
  from tensorflow.lite.python import interpreter
  from tensorflow.lite.python.metrics import metrics
  from tensorflow.python.util import tf_export
label_desc: Use image node_id 14 for calling the QuantizationDebugger obj's underlying member method code with example usage: obj._set_input_tensors(interpreter, tensor_data, initialize) without return types