class_docstr: Head used for fewshot metrics.
  
  There are no trainable parameters in this module.
  
  Attributes:
    pooling_type: type of the bottleneck. Options are 'gap', 'gmp', 'gsp',
    'token'.
class_docstr_tok: ["Head","used","for","fewshot","metrics",".","There","are","no","trainable","parameters","in","this","module",".","Attributes",":","pooling_type",":","type","of","the","bottleneck",".","Options","are","'gap","'",",","'gmp","'",",","'gsp","'",",","'token","'","."]
code: def __call__(self, x: jnp.ndarray, *, train: bool) -> jnp.ndarray:
      del train
  
      x = get_bottleneck_representation(x, self.pooling_type)
  
      return x
  
imports: import functools
  from typing import Any, Callable, Optional, Sequence, Tuple, Union
  import flax.linen
  import jax
  from jax.nn import initializers
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.model_lib.layers import attention_layers
  from scenic.model_lib.layers import nn_layers
  from scenic.projects.baselines import vit
  from scenic.projects.polyvit import polyvit_base_model
  from scenic.projects.vivit import model
label_desc: Use image node_id 1 for calling the FewshotHead obj's underlying member method code with example usage: obj.__call__(x) and returns: x