src/transformers/models/mobilebert/modeling_mobilebert.py/Bottleneck/forward
class Bottleneck:    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
        # This method can return three different tuples of values. These different values make use of bottlenecks,
        # which are linear layers used to project the hidden states to a lower-dimensional vector, reducing memory
        # usage. These linear layer have weights that are learned during training.
        #
        # If `config.use_bottleneck_attention`, it will return the result of the bottleneck layer four times for the
        # key, query, value, and "layer input" to be used by the attention layer.
        # This bottleneck is used to project the hidden. This last layer input will be used as a residual tensor
        # in the attention self output, after the attention scores have been computed.
        #
        # If not `config.use_bottleneck_attention` and `config.key_query_shared_bottleneck`, this will return
        # four values, three of which have been passed through a bottleneck: the query and key, passed through the same
        # bottleneck, and the residual layer to be applied in the attention self output, through another bottleneck.
        #
        # Finally, in the last case, the values for the query, key and values are the hidden states without bottleneck,
        # and the residual layer will be this value passed through a bottleneck.

        bottlenecked_hidden_states = self.input(hidden_states)
        if self.use_bottleneck_attention:
            return (bottlenecked_hidden_states,) * 4
        elif self.key_query_shared_bottleneck:
            shared_attention_input = self.attention(hidden_states)
            return (shared_attention_input, shared_attention_input, hidden_states, bottlenecked_hidden_states)
        else:
            return (hidden_states, hidden_states, hidden_states, bottlenecked_hidden_states)