class_docstr: Class for few-shot evaluation.
class_docstr_tok: ["Class","for","few-shot","evaluation","."]
code: def __init__(
      self,
      representation_fn,
      fewshot_config,
      backend: str = "pmap",
      devices: Optional[jnp.ndarray] = None,
      out_shardings: Optional[PyTree] = None,
  ):
      self.shots = fewshot_config.shots
      self.l2_regs = fewshot_config.l2_regs
      self.local_batch_size = (
          fewshot_config.batch_size // jax.process_count()
      )
      self.pp_tr = fewshot_config.pp_train
      self.pp_te = fewshot_config.pp_eval
      self.walk_first = fewshot_config.walk_first
      self._datasets = {}  # This will be our cache for lazy loading.
  
      self.backend = backend
      assert self.backend in {
          "pmap",
          "jit",
      }, f"Unsupported backend: {self.backend}. Must be one of [pmap, jit]."
      if self.backend == "jit":
          assert (
              devices is not None
          ), "Devices must be provided when using jit."
      self.devices = devices
  
      if self.backend == "pmap":
          self.repr_fn = jax.pmap(
              representation_fn, donate_argnums=(1,), axis_name="batch"
          )
      elif self.backend == "jit":
          self.repr_fn = jax.jit(
              representation_fn,
              donate_argnums=(1,),
              out_shardings=out_shardings,
          )
  
imports: import functools
  from typing import Any, Optional, Tuple
  from absl import logging
  from clu import metric_writers
  from flax import jax_utils
  import jax
  import jax.numpy
  import numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib.big_transfer import builder
  from scenic.train_lib import train_utils
  import tensorflow_datasets
label_desc: Use image node_id 1 to create a new FewShotEvaluator object  with example: obj = FewShotEvaluator(representation_fn, fewshot_config, backend, devices, out_shardings)