src/transformers/models/mobilebert/configuration_mobilebert.py/MobileBertConfig/__init__
class MobileBertConfig:    def __init__(
        self,
        vocab_size=30522,
        hidden_size=512,
        num_hidden_layers=24,
        num_attention_heads=4,
        intermediate_size=512,
        hidden_act="relu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        embedding_size=128,
        trigram_input=True,
        use_bottleneck=True,
        intra_bottleneck_size=128,
        use_bottleneck_attention=False,
        key_query_shared_bottleneck=True,
        num_feedforward_networks=4,
        normalization_type="no_norm",
        classifier_activation=True,
        classifier_dropout=None,
        **kwargs
    ):
        super().__init__(pad_token_id=pad_token_id, **kwargs)

        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.embedding_size = embedding_size
        self.trigram_input = trigram_input
        self.use_bottleneck = use_bottleneck
        self.intra_bottleneck_size = intra_bottleneck_size
        self.use_bottleneck_attention = use_bottleneck_attention
        self.key_query_shared_bottleneck = key_query_shared_bottleneck
        self.num_feedforward_networks = num_feedforward_networks
        self.normalization_type = normalization_type
        self.classifier_activation = classifier_activation

        if self.use_bottleneck:
            self.true_hidden_size = intra_bottleneck_size
        else:
            self.true_hidden_size = hidden_size

        self.classifier_dropout = classifier_dropout