keras/src/layers/core/einsum_dense.py/EinsumDense/save_own_variables
class EinsumDense:    def save_own_variables(self, store):
        # Do nothing if the layer isn't yet built
        if not self.built:
            return
        # The keys of the `store` will be saved as determined because the
        # default ordering will change after quantization
        kernel_value, kernel_scale = self._get_kernel_with_merged_lora()
        target_variables = [kernel_value]
        if self.bias is not None:
            target_variables.append(self.bias)
        if self.quantization_mode is not None:
            if self.quantization_mode == "int8":
                target_variables.append(kernel_scale)
            elif self.quantization_mode == "float8":
                target_variables.append(self.inputs_scale)
                target_variables.append(self.inputs_amax_history)
                target_variables.append(self.kernel_scale)
                target_variables.append(self.kernel_amax_history)
                target_variables.append(self.outputs_grad_scale)
                target_variables.append(self.outputs_grad_amax_history)
            else:
                raise self._quantization_mode_error(self.quantization_mode)
        for i, variable in enumerate(target_variables):
            store[str(i)] = variable