src/transformers/models/t5/modeling_t5.py/T5LayerNorm/forward
class T5LayerNorm:    def forward(self, hidden_states):
        # layer norm should always be calculated in float32
        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)

        # convert into float16 if necessary
        if self.weight.dtype == torch.float16:
            hidden_states = hidden_states.to(torch.float16)
        return self.weight * hidden_states