class_docstr: In memory random sample iterator implemented in JAX.
  
  Loads the whole dataset in memory and performs random sampling with
  replacement of batches of `batch_size`.
  This class provides much faster sampling functionality compared to using
  an iterator on tf.data.Dataset.
code: def __next__(self) -> Any:
      data, self._key = self._sample(self._key)
      return data
  
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 2 for calling the JaxInMemoryRandomSampleIterator obj's underlying member method code with example usage: obj.__next__() and returns: data