from efficientnet_pytorch import EfficientNet as EffNet
import torch.nn as nn


class EfficientNet(nn.Module):
    """ Neural network """
    def __init__(self, input_shape, num_classes=23, pretrained=True, b=0):
        super().__init__()
        self.input_shape = input_shape
        model_str = f'efficientnet-b{b}'
        self.efficient_net = EffNet.from_pretrained(model_str, num_classes=num_classes) \
                if pretrained else EffNet.from_name(model_str, num_classes=num_classes)

    def forward(self, x):
        """ Forward pass for your network. """
        x = self.efficient_net(x)
        return x

cffb5cea9ba8df54b01e6b28403617d961a75e78
<mask token>


class EfficientNet(nn.Module):
    <mask token>
    <mask token>
    <mask token>

<mask token>


class EfficientNet(nn.Module):
    <mask token>

    def __init__(self, input_shape, num_classes=23, pretrained=True, b=0):
        super().__init__()
        self.input_shape = input_shape
        model_str = f'efficientnet-b{b}'
        self.efficient_net = EffNet.from_pretrained(model_str, num_classes=
            num_classes) if pretrained else EffNet.from_name(model_str,
            num_classes=num_classes)

    def forward(self, x):
        """ Forward pass for your network. """
        x = self.efficient_net(x)
        return x

<mask token>


class EfficientNet(nn.Module):
    """ Neural network """

    def __init__(self, input_shape, num_classes=23, pretrained=True, b=0):
        super().__init__()
        self.input_shape = input_shape
        model_str = f'efficientnet-b{b}'
        self.efficient_net = EffNet.from_pretrained(model_str, num_classes=
            num_classes) if pretrained else EffNet.from_name(model_str,
            num_classes=num_classes)

    def forward(self, x):
        """ Forward pass for your network. """
        x = self.efficient_net(x)
        return x

from efficientnet_pytorch import EfficientNet as EffNet
import torch.nn as nn


class EfficientNet(nn.Module):
    """ Neural network """

    def __init__(self, input_shape, num_classes=23, pretrained=True, b=0):
        super().__init__()
        self.input_shape = input_shape
        model_str = f'efficientnet-b{b}'
        self.efficient_net = EffNet.from_pretrained(model_str, num_classes=
            num_classes) if pretrained else EffNet.from_name(model_str,
            num_classes=num_classes)

    def forward(self, x):
        """ Forward pass for your network. """
        x = self.efficient_net(x)
        return x

from efficientnet_pytorch import EfficientNet as EffNet
import torch.nn as nn


class EfficientNet(nn.Module):
    """ Neural network """
    def __init__(self, input_shape, num_classes=23, pretrained=True, b=0):
        super().__init__()
        self.input_shape = input_shape
        model_str = f'efficientnet-b{b}'
        self.efficient_net = EffNet.from_pretrained(model_str, num_classes=num_classes) \
                if pretrained else EffNet.from_name(model_str, num_classes=num_classes)

    def forward(self, x):
        """ Forward pass for your network. """
        x = self.efficient_net(x)
        return x
