keras/src/models/model.py/Model/load_weights
class Model:    def load_weights(self, filepath, skip_mismatch=False, **kwargs):
        """Load weights from a file saved via `save_weights()`.

        Weights are loaded based on the network's
        topology. This means the architecture should be the same as when the
        weights were saved. Note that layers that don't have weights are not
        taken into account in the topological ordering, so adding or removing
        layers is fine as long as they don't have weights.

        **Partial weight loading**

        If you have modified your model, for instance by adding a new layer
        (with weights) or by changing the shape of the weights of a layer,
        you can choose to ignore errors and continue loading
        by setting `skip_mismatch=True`. In this case any layer with
        mismatching weights will be skipped. A warning will be displayed
        for each skipped layer.

        Args:
            filepath: String, path to the weights file to load.
                It can either be a `.weights.h5` file
                or a legacy `.h5` weights file.
            skip_mismatch: Boolean, whether to skip loading of layers where
                there is a mismatch in the number of weights, or a mismatch in
                the shape of the weights.
        """
        saving_api.load_weights(
            self, filepath, skip_mismatch=skip_mismatch, **kwargs
        )