class_docstr: 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.
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docstr: Gets input tensor details.
  
  Returns:
    A dictionary from input name to tensor details where 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 unknown, 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.
code: def get_input_details(self):
      """Gets input tensor details.
  
      Returns:
        A dictionary from input name to tensor details where 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 unknown, 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.
      """
      result = {}
      for input_name, tensor_index in self._inputs.items():
          result[
              input_name
          ] = self._interpreter._get_tensor_details(  # pylint: disable=protected-access
              tensor_index, self._subgraph_index
          )
      return result
  
imports: import ctypes
  import enum
  import os
  import platform
  import sys
  import numpy
label_desc: Use image node_id 3 for calling the SignatureRunner obj's underlying member method code with example usage: obj.get_input_details() and returns: result