src/transformers/models/regnet/modeling_tf_regnet.py/TFRegNetConvLayer/__init__
class TFRegNetConvLayer:    def __init__(
        self,
        out_channels: int,
        kernel_size: int = 3,
        stride: int = 1,
        groups: int = 1,
        activation: Optional[str] = "relu",
        **kwargs,
    ):
        super().__init__(**kwargs)
        # The padding and conv has been verified in
        # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
        self.padding = tf.keras.layers.ZeroPadding2D(padding=kernel_size // 2)
        self.convolution = tf.keras.layers.Conv2D(
            filters=out_channels,
            kernel_size=kernel_size,
            strides=stride,
            padding="VALID",
            groups=groups,
            use_bias=False,
            name="convolution",
        )
        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.identity