class_docstr: This scaling object represents the following scaling equations for inputs (x)
  and outputs (y)
  
  .. math::
      \begin{align*}
      x_i^{scaled} = \frac{(x_i-x_i^{offset})}{x_i^{factor}} \\
      y_i^{scaled} = \frac{(y_i-y_i^{offset})}{y_i^{factor}}
      \end{align*}
  
  Parameters
  ----------
  offset_inputs : array-like
      Array of the values of the offsets for each input to the network
  factor_inputs : array-like
      Array of the scaling factors (division) for each input to the network
  offset_outputs : array-like
      Array of the values of the offsets for each output from the network
  factor_outputs : array-like
      Array of the scaling factors (division) for each output from the network
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docstr: Get the unscaled input expressions of the scaled input variables.
docstr_tok: ["Get","the","unscaled","input","expressions","of","the","scaled","input","variables","."]
code: def get_unscaled_input_expressions(self, scaled_input_vars):
      """
      Get the unscaled input expressions of the scaled input variables.
      """
      sorted_keys = sorted(scaled_input_vars.keys())
      if (
          sorted(self.__x_offset) != sorted_keys
          or sorted(self.__x_factor) != sorted_keys
      ):
          raise ValueError(
              "get_scaled_input_expressions called with input_vars"
              " that do not have the same indices as offset_inputs"
              " or factor_inputs.\n"
              "Keys in input_vars: {}\n"
              "Keys in offset_inputs: {}\n"
              "Keys in offset_factor: {}".format(
                  sorted_keys,
                  sorted(self.__x_offset),
                  sorted(self.__x_factor),
              )
          )
  
      scaled_x = scaled_input_vars
      return {
          k: scaled_x[k] * self.__x_factor[k] + self.__x_offset[k]
          for k in scaled_x.keys()
      }
  
file_path: OMLT/src/omlt/scaling.py
imports: import abc
label_desc: Use image node_id 3 for calling the OffsetScaling obj's underlying member method code with example usage: obj.get_unscaled_input_expressions(scaled_input_vars) and returns: unknown