The multilayer perceptron (MLP) for a vision Transformer encoder can be implemented using standard neural network modules in frameworks like PyTorch or JAX. The architecture consists of two dense layers. The first dense layer applies a linear transformation followed by a Gaussian Error Linear Unit (GELU) activation and a dropout layer for regularization. The second dense layer projects the features to the desired output dimension, and is subsequently followed by another dropout layer.

# PyTorch
class ViTMLP(nn.Module):
    def __init__(self, mlp_num_hiddens, mlp_num_outputs, dropout=0.5):
        super().__init__()
        self.dense1 = nn.LazyLinear(mlp_num_hiddens)
        self.gelu = nn.GELU()
        self.dropout1 = nn.Dropout(dropout)
        self.dense2 = nn.LazyLinear(mlp_num_outputs)
        self.dropout2 = nn.Dropout(dropout)

    def forward(self, x):
        return self.dropout2(self.dense2(self.dropout1(self.gelu(
            self.dense1(x)))))

# JAX
class ViTMLP(nn.Module):
    mlp_num_hiddens: int
    mlp_num_outputs: int
    dropout: float = 0.5

    @nn.compact
    def __call__(self, x, training=False):
        x = nn.Dense(self.mlp_num_hiddens)(x)
        x = nn.gelu(x)
        x = nn.Dropout(self.dropout, deterministic=not training)(x)
        x = nn.Dense(self.mlp_num_outputs)(x)
        x = nn.Dropout(self.dropout, deterministic=not training)(x)
        return x