def update_missing_metadata()

in src/neo_loader/sklearn_model_loader.py [0:0]

• 10 lines of code
• 6 McCabe index (conditional complexity)

    def update_missing_metadata(self):
        # Replace "input" data shape with actual shapes from TVM module (could be "input" still).
        # For ambigious columns, there may be  some combination of "input_float" and "input_string"
        # (one of these or both) depending on which transfers are used. If input wasn't used, TVM
        # deletes it so this is why we use the TVM module as source of ground truth.
        input_shape = self.data_shape["input"]
        del self.data_shape["input"]
        for inp in self._relay_module_object["main"].params:
            self.data_shape[inp.name_hint] = input_shape
        super().update_missing_metadata()
        for input_config in self._metadata["Inputs"]:
            input_config["dtype"] = FLOAT_32
        for output_config in self._metadata["Outputs"]:
            output_config["name"] = "output"