A complete vision Transformer can be implemented by assembling patch embeddings, learnable positional embeddings, a stack of encoder blocks, and a classification head. The implementation involves creating a sequential forward pass: extracting patch embeddings, concatenating a learnable <cls> token, adding learnable positional embeddings, applying dropout, passing the sequence through the stacked blocks, and finally using the <cls> token's output state for classification.

# PyTorch
class ViT(d2l.Classifier):
    """Vision Transformer."""
    def __init__(self, img_size, patch_size, num_hiddens, mlp_num_hiddens,
                 num_heads, num_blks, emb_dropout, blk_dropout, lr=0.1,
                 use_bias=False, num_classes=10):
        super().__init__()
        self.save_hyperparameters()
        self.patch_embedding = PatchEmbedding(
            img_size, patch_size, num_hiddens)
        self.cls_token = nn.Parameter(torch.zeros(1, 1, num_hiddens))
        num_steps = self.patch_embedding.num_patches + 1  # Add the cls token
        # Positional embeddings are learnable
        self.pos_embedding = nn.Parameter(
            torch.randn(1, num_steps, num_hiddens))
        self.dropout = nn.Dropout(emb_dropout)
        self.blks = nn.Sequential()
        for i in range(num_blks):
            self.blks.add_module(f"{i}", ViTBlock(
                num_hiddens, num_hiddens, mlp_num_hiddens,
                num_heads, blk_dropout, use_bias))
        self.head = nn.Sequential(nn.LayerNorm(num_hiddens),
                                  nn.Linear(num_hiddens, num_classes))

    def forward(self, X):
        X = self.patch_embedding(X)
        X = torch.cat((self.cls_token.expand(X.shape[0], -1, -1), X), 1)
        X = self.dropout(X + self.pos_embedding)
        for blk in self.blks:
            X = blk(X)
        return self.head(X[:, 0])

# JAX
class ViT(d2l.Classifier):
    """Vision Transformer."""
    img_size: int
    patch_size: int
    num_hiddens: int
    mlp_num_hiddens: int
    num_heads: int
    num_blks: int
    emb_dropout: float
    blk_dropout: float
    lr: float = 0.1
    use_bias: bool = False
    num_classes: int = 10
    training: bool = False

    def setup(self):
        self.patch_embedding = PatchEmbedding(self.img_size, self.patch_size,
                                              self.num_hiddens)
        self.cls_token = self.param('cls_token', nn.initializers.zeros,
                                    (1, 1, self.num_hiddens))
        num_steps = self.patch_embedding.num_patches + 1  # Add the cls token
        # Positional embeddings are learnable
        self.pos_embedding = self.param('pos_embed', nn.initializers.normal(),
                                        (1, num_steps, self.num_hiddens))
        self.blks = [ViTBlock(self.num_hiddens, self.mlp_num_hiddens,
                              self.num_heads, self.blk_dropout, self.use_bias)
                    for _ in range(self.num_blks)]
        self.head = nn.Sequential([nn.LayerNorm(), nn.Dense(self.num_classes)])

    @nn.compact
    def __call__(self, X):
        X = self.patch_embedding(X)
        X = jnp.concatenate((jnp.tile(self.cls_token, (X.shape[0], 1, 1)), X), 1)
        X = nn.Dropout(emb_dropout, deterministic=not self.training)(X + self.pos_embedding)
        for blk in self.blks:
            X = blk(X, training=self.training)
        return self.head(X[:, 0])