code: def _fewshot_acc_fn(
      x: jnp.ndarray,
      y: jnp.ndarray,
      x_test: jnp.ndarray,
      y_test: jnp.ndarray,
      l2_reg: float,
      num_classes: int,
      target_is_one_hot: bool = False,
      stddev_constant: float = 1e-5,
  ) -> float:
      """Computes (x,y) linear regression accuracy on (x_test, y_test).
  
      Args:
        x: An [n_examples, n_classes] matrix of feature representations. This will
          be whitened before computing linear regression.
        y: Array of labels. Shape is either [n_examples] or [n_examples, n_classes].
          In the latter case, target_is_one_hot must be True.
        x_test: An [n_test_examples, n_classes] matrix of feature representations.
          Will be whitened before computing linear regression.
        y_test: Array of labels. Shape is either [n_examples] or [n_examples,
          n_classes]. In the latter case, target_is_one_hot must be True.
        l2_reg: L2 regularisation co-efficient to apply when computing linear
          regression (also known as "ridge regression").
        num_classes: The number of classes in the dataset. Used to convert y to a
          one-hot representation if not already.
        target_is_one_hot: If the labels, y, are already one-hot or not.
        stddev_constant: Small constant to add when computing the standard deviation
          to avoid it being 0.
  
      Returns:
        The accuracy or precision@1 (for one-hot labels), after computing linear
          regression.
      """
  
      def preprocess_features(
          data: jnp.ndarray, mean: jnp.ndarray, std: jnp.ndarray
      ) -> jnp.ndarray:
          """Whitens features and adds a bias term."""
          data_whitened = (data - mean) / std
          # Add a constant feature for the bias, large so it's almost unregularized.
          data_whitened_bias = jnp.pad(
              data_whitened,
              ((0, 0), (0, 1)),
              constant_values=_BIAS_CONSTANT,
          )
          return data_whitened_bias
  
      mean = jnp.mean(x, axis=0, keepdims=True)
      std = jnp.std(x, axis=0, keepdims=True) + stddev_constant
  
      x_whitened = preprocess_features(x, mean, std)
      x_test_whitened = preprocess_features(x_test, mean, std)
  
      # Solve linear regression problem.
      if not target_is_one_hot:
          y_one_hot = jax.nn.one_hot(y, num_classes)
      else:
          y_one_hot = y
      y_rescaled = 2.0 * y_one_hot - 1.0
      w = jnp.linalg.solve(
          x_whitened.T @ x_whitened
          + jnp.eye(x_whitened.shape[1]) * l2_reg,
          x_whitened.T @ y_rescaled,
      )
  
      if target_is_one_hot:
          # Compute the precision@1 for multilabel datasets. This is the same as
          # accuracy if there is one active label.
          preds = x_test_whitened @ w
          top1_idx = jnp.argmax(preds, axis=-1)
          top1_correct = jnp.take_along_axis(
              y_test, top1_idx[..., None], axis=-1
          )
          top1_correct = jnp.squeeze(top1_correct)
          return jnp.mean(
              top1_correct
          )  # pytype: disable=bad-return-type  # jnp-type
      else:
          # Predict test-set values and measure their accuracy.
          preds = jnp.argmax(x_test_whitened @ w, axis=1)
          return jnp.mean(preds == y_test)
  
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 4 for calling a global function with example usage: _fewshot_acc_fn(x, y, x_test, y_test, l2_reg, num_classes, target_is_one_hot, stddev_constant) and returns: data_whitened_bias, jnp, jnp