class_docstr: Vision Transformer model for LOCA training.
  
  Attributes:
  mlp_dim: Dimension of the mlp on top of attention block.
  num_layers: Number of layers.
  num_heads: Number of self-attention heads.
  patches: Configuration of the patches extracted in the stem of the model.
  hidden_size: Size of the hidden state of the output of model's stem.
  n_ref_positions: Number of position in the reference view.
  apply_cluster_loss: Whether to apply the clustering loss.
  head_hidden_dim: Dimension of the hidden layer in the projection mlp.
  head_bottleneck_dim: Dimension of the bottleneck.
  head_output_dim: Dimension of the output ("number of prototypes").
  dropout_rate: Dropout rate.
  attention_dropout_rate: Dropout for attention heads.
  stochastic_depth: Stochastic depth.
  posembs: Positional embedding size.
  dtype: JAX data type for activations.
code: def __call__(
      self,
      x: jnp.ndarray,
      *,
      inputs_kv: Optional[jnp.ndarray] = None,
      train: bool,
      seqlen: int = -1,
      use_pe: bool = True,
      drop_moment: str = "early",
      seqlen_selection: str = "unstructured",
      debug: bool = False,
  ):
      del debug
      # Input image -> sequence of patch tokens.
      to_token_fn = ToTokenSequence(
          patches=self.patches,
          hidden_size=self.hidden_size,
          posembs=self.posembs,
      )
      x, idx_kept_tokens = to_token_fn(
          x,
          seqlen=seqlen if drop_moment == "early" else -1,
          positional_embedding=None if use_pe else "pe_not_in_use",
          seqlen_selection=seqlen_selection,
      )
  
      # ViT Encoder.
      for lyr in range(self.num_layers):
          x = vit.Encoder1DBlock(
              mlp_dim=self.mlp_dim,
              num_heads=self.num_heads,
              dropout_rate=self.dropout_rate,
              attention_dropout_rate=self.attention_dropout_rate,
              stochastic_depth=(lyr / max(self.num_layers - 1, 1))
              * self.stochastic_depth,
              name=f"encoderblock_{lyr}",
              dtype=jax.dtypes.canonicalize_dtype(self.dtype),
          )(x, deterministic=not train)
      x = nn.LayerNorm(name="encoder_norm")(x)
  
      # Optionally apply a clustering prediction loss.
      cluster_pred_outputs = None
      if self.apply_cluster_loss:
          cluster_pred_outputs = ProjectionHead(
              hidden_dim=self.head_hidden_dim,
              bottleneck_dim=self.head_bottleneck_dim,
              output_dim=self.head_output_dim,
              name="projection_head_for_clustering_prediction",
          )(x, train).reshape((-1, self.head_output_dim))
  
      patches_repr = x
      # Drop some tokens (in the reference view).
      if drop_moment == "late":
          rng = self.make_rng("droptok")
          idx_kept_tokens = token_indexes_not_to_drop(
              seqlen, self.n_ref_positions, seqlen_selection, rng
          )
          if len(idx_kept_tokens) < self.n_ref_positions:
              patches_repr = jnp.take(
                  patches_repr, idx_kept_tokens, axis=1
              )
  
      # Query patches look at those of the reference through cross attention.
      if inputs_kv is None:
          inputs_kv = copy.deepcopy(patches_repr)
      x = CrossAttentionEncoderBlock(
          mlp_dim=self.mlp_dim,
          num_heads=self.num_heads,
          dropout_rate=self.dropout_rate,
          attention_dropout_rate=self.attention_dropout_rate,
          name="cross_attention_block",
          dtype=jax.dtypes.canonicalize_dtype(self.dtype),
      )(x, inputs_kv=inputs_kv, deterministic=not train)
      x = nn.LayerNorm(name="final_norm")(x)
      x = nn.Dense(self.n_ref_positions, name="position_predictor")(x)
      return x, cluster_pred_outputs, patches_repr, idx_kept_tokens
  
imports: import copy
  import functools
  from typing import Any, Optional, Tuple
  import flax.linen
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.model_lib.base_models import base_model
  from scenic.model_lib.base_models import classification_model
  from scenic.model_lib.base_models import model_utils
  from scenic.model_lib.layers import attention_layers
  from scenic.model_lib.layers import nn_layers
  from scenic.projects.baselines import vit
label_desc: Use image node_id 1 for calling the ViT4LOCA obj's underlying member method code with example usage: obj.__call__(x) and returns: x,  cluster_pred_outputs,  patches_repr,  idx_kept_tokens