coremltools/converters/keras/_keras2_converter.py [566:606]:
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    builder.add_optionals(graph.optional_inputs, graph.optional_outputs)

    # Add classifier classes (if applicable)
    if is_classifier:
        classes_in = class_labels
        if isinstance(classes_in, str):
            import os

            if not os.path.isfile(classes_in):
                raise ValueError(
                    "Path to class labels (%s) does not exist." % classes_in
                )
            with open(classes_in, "r") as f:
                classes = f.read()
            classes = classes.splitlines()
        elif type(classes_in) is list:  # list[int or str]
            classes = classes_in
        else:
            raise ValueError(
                "Class labels must be a list of integers / strings, or a file path"
            )

        if predicted_feature_name is not None:
            builder.set_class_labels(
                classes,
                predicted_feature_name=predicted_feature_name,
                prediction_blob=predicted_probabilities_output,
            )
        else:
            builder.set_class_labels(classes)

    # Set pre-processing parameters
    builder.set_pre_processing_parameters(
        image_input_names=image_input_names,
        is_bgr=is_bgr,
        red_bias=red_bias,
        green_bias=green_bias,
        blue_bias=blue_bias,
        gray_bias=gray_bias,
        image_scale=image_scale,
    )
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coremltools/converters/keras/_keras_converter.py [312:352]:
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    builder.add_optionals(graph.optional_inputs, graph.optional_outputs)

    # Add classifier classes (if applicable)
    if is_classifier:
        classes_in = class_labels
        if isinstance(classes_in, str):
            import os

            if not os.path.isfile(classes_in):
                raise ValueError(
                    "Path to class labels (%s) does not exist." % classes_in
                )
            with open(classes_in, "r") as f:
                classes = f.read()
            classes = classes.splitlines()
        elif type(classes_in) is list:  # list[int or str]
            classes = classes_in
        else:
            raise ValueError(
                "Class labels must be a list of integers / strings, or a file path"
            )

        if predicted_feature_name is not None:
            builder.set_class_labels(
                classes,
                predicted_feature_name=predicted_feature_name,
                prediction_blob=predicted_probabilities_output,
            )
        else:
            builder.set_class_labels(classes)

    # Set pre-processing paramsters
    builder.set_pre_processing_parameters(
        image_input_names=image_input_names,
        is_bgr=is_bgr,
        red_bias=red_bias,
        green_bias=green_bias,
        blue_bias=blue_bias,
        gray_bias=gray_bias,
        image_scale=image_scale,
    )
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