src/transformers/models/efficientnet/configuration_efficientnet.py/EfficientNetConfig/__init__
class EfficientNetConfig:    def __init__(
        self,
        num_channels: int = 3,
        image_size: int = 600,
        width_coefficient: float = 2.0,
        depth_coefficient: float = 3.1,
        depth_divisor: int = 8,
        kernel_sizes: List[int] = [3, 3, 5, 3, 5, 5, 3],
        in_channels: List[int] = [32, 16, 24, 40, 80, 112, 192],
        out_channels: List[int] = [16, 24, 40, 80, 112, 192, 320],
        depthwise_padding: List[int] = [],
        strides: List[int] = [1, 2, 2, 2, 1, 2, 1],
        num_block_repeats: List[int] = [1, 2, 2, 3, 3, 4, 1],
        expand_ratios: List[int] = [1, 6, 6, 6, 6, 6, 6],
        squeeze_expansion_ratio: float = 0.25,
        hidden_act: str = "swish",
        hidden_dim: int = 2560,
        pooling_type: str = "mean",
        initializer_range: float = 0.02,
        batch_norm_eps: float = 0.001,
        batch_norm_momentum: float = 0.99,
        dropout_rate: float = 0.5,
        drop_connect_rate: float = 0.2,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.num_channels = num_channels
        self.image_size = image_size
        self.width_coefficient = width_coefficient
        self.depth_coefficient = depth_coefficient
        self.depth_divisor = depth_divisor
        self.kernel_sizes = kernel_sizes
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.depthwise_padding = depthwise_padding
        self.strides = strides
        self.num_block_repeats = num_block_repeats
        self.expand_ratios = expand_ratios
        self.squeeze_expansion_ratio = squeeze_expansion_ratio
        self.hidden_act = hidden_act
        self.hidden_dim = hidden_dim
        self.pooling_type = pooling_type
        self.initializer_range = initializer_range
        self.batch_norm_eps = batch_norm_eps
        self.batch_norm_momentum = batch_norm_momentum
        self.dropout_rate = dropout_rate
        self.drop_connect_rate = drop_connect_rate
        self.num_hidden_layers = sum(num_block_repeats) * 4