pytorchvideo/models/resnet.py [131:148]:
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        in_channels=dim_inner, out_channels=dim_out, kernel_size=(1, 1, 1), bias=False
    )
    norm_c = (
        None
        if norm is None
        else norm(num_features=dim_out, eps=norm_eps, momentum=norm_momentum)
    )

    return BottleneckBlock(
        conv_a=conv_a,
        norm_a=norm_a,
        act_a=act_a,
        conv_b=conv_b,
        norm_b=norm_b,
        act_b=act_b,
        conv_c=conv_c,
        norm_c=norm_c,
    )
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pytorchvideo/models/x3d.py [211:228]:
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        in_channels=dim_inner, out_channels=dim_out, kernel_size=(1, 1, 1), bias=False
    )
    norm_c = (
        None
        if norm is None
        else norm(num_features=dim_out, eps=norm_eps, momentum=norm_momentum)
    )

    return BottleneckBlock(
        conv_a=conv_a,
        norm_a=norm_a,
        act_a=act_a,
        conv_b=conv_b,
        norm_b=norm_b,
        act_b=act_b,
        conv_c=conv_c,
        norm_c=norm_c,
    )
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