class_docstr: Projection head.
  
  Attributes:
    hidden_dim: Dimension of the hidden layer in the projection mlp.
    bottleneck_dim: Dimension of the bottleneck.
    output_dim: Dimension of the output ("number of prototypes").
    normalize_last_layer: Normalize the last layer of prototypes.
    use_bn: Use batch normalizations.
    n_layers: Depth of the projection head.
code: def __call__(self, x: jnp.ndarray, train: bool) -> jnp.ndarray:
      for i in range(self.n_layers):
          x = nn.Dense(self.hidden_dim)(x)
          x = nn.gelu(x)
          x = nn_layers.IdentityLayer(name=f"mlp_{i}")(x)
      x = nn.Dense(self.bottleneck_dim)(x)
      # Normalize.
      x /= jnp.linalg.norm(x, axis=-1, keepdims=True)
      x = WeightNormDense(
          self.output_dim,
          use_bias=False,
          name="prototypes",
          kernel_init=norm_kernel_init_fn,
      )(x)
      return x
  
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 ProjectionHead obj's underlying member method code with example usage: obj.__call__(x, train) and returns: x