def init_weights(self):
        """[Note: On ``init_weights`` vs.

        ``reset_parameters``]
        Modules may define ``reset_parameters`` to initialize parameter values.
        ``reset_parameters`` is meant to only initialize directly owned
        parameters/buffers, not those of their child modules, and it can be
        used to give the initial values for these tensors.
        Separately, users may want custom initialization for their modules,
        different from that in ``reset_parameters``. For this, we define
        ``init_weights``. We only call it in the constructor of this
        ``Transformer`` root module to avoid reinitializing tensors.

        """
        with torch.device(self.freqs_cis.device):
            self.freqs_cis = self._precompute_freqs_cis()
        nn.init.normal_(self.tok_embeddings.weight)
        for layer in self.layers.values():
            layer.init_weights()
        self.norm.reset_parameters()
        final_out_std = self.model_args.dim**-0.5
        cutoff_factor = 3
        nn.init.trunc_normal_(
            self.output.weight,
            mean=0.0,
            std=final_out_std,
            a=-cutoff_factor * final_out_std,
            b=cutoff_factor * final_out_std,
        )
[Note: On ``init_weights`` vs.

        ``reset_parameters``]
        Modules may define ``reset_parameters`` to initialize parameter values.
        ``reset_parameters`` is meant to only initialize directly owned
        parameters/buffers, not those of their child modules, and it can be
        used to give the initial values for these tensors.
        Separately, users may want custom initialization for their modules,
        different from that in ``reset_parameters``. For this, we define
        ``init_weights``. We only call it in the constructor of this
        ``Transformer`` root module to avoid reinitializing tensors.

        
examples/fabric/tensor_parallel/model.py