src/transformers/models/resnet/modeling_tf_resnet.py/TFResNetShortCut/__init__
class TFResNetShortCut:    def __init__(self, out_channels: int, stride: int = 2, **kwargs) -> None:
        super().__init__(**kwargs)
        self.convolution = tf.keras.layers.Conv2D(
            out_channels, kernel_size=1, strides=stride, use_bias=False, name="convolution"
        )
        # Use same default momentum and epsilon as PyTorch equivalent
        self.normalization = tf.keras.layers.BatchNormalization(epsilon=1e-5, momentum=0.9, name="normalization")