code: def _pad(x: jnp.ndarray) -> jnp.ndarray:
      if len(x.shape) != 2:
          return x
      # Find a more scientific way to find this threshold (30). Depending on various
      # conditions for low enough sizes the excessive copying is not triggered.
      if x.shape[-1] % _BEST_DIVISOR != 0 and x.shape[-1] > 30:
          n = _BEST_DIVISOR - (x.shape[-1] % _BEST_DIVISOR)
          x = np.pad(x, [(0, 0)] * (x.ndim - 1) + [(0, n)], "constant")
      return x
  
imports: import logging
  from typing import Any, Iterator, Optional, Tuple, Sequence
  from acme import specs
  from acme import types
  from flax import jax_utils
  import jax
  import jax.numpy
  import numpy
  import rlds
  import tensorflow
  import tensorflow_datasets
label_desc: Use image node_id 6 for calling a global function with example usage: _pad(x) and returns: x, x