src/transformers/models/resnet/modeling_tf_resnet.py/TFResNetConvLayer/__init__
class TFResNetConvLayer:    def __init__(
        self, out_channels: int, kernel_size: int = 3, stride: int = 1, activation: str = "relu", **kwargs
    ) -> None:
        super().__init__(**kwargs)
        self.pad_value = kernel_size // 2
        self.conv = tf.keras.layers.Conv2D(
            out_channels, kernel_size=kernel_size, strides=stride, padding="valid", 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")
        self.activation = ACT2FN[activation] if activation is not None else tf.keras.layers.Activation("linear")