code: def get_dataset_and_shards(
      config: "AlgorithmConfig", num_workers: int = 0
  ) -> Tuple[ray.data.Dataset, List[ray.data.Dataset]]:
      """Returns a dataset and a list of shards.
  
      This function uses algorithm configs to create a dataset and a list of shards.
      The following config keys are used to create the dataset:
          input: The input type should be "dataset".
          input_config: A dict containing the following key and values:
              `format`: str, speciifies the format of the input data. This will be the
              format that ray dataset supports. See ray.data.Dataset for
              supported formats. Only "parquet" or "json" are supported for now.
              `paths`: str, a single string or a list of strings. Each string is a path
              to a file or a directory holding the dataset. It can be either a local path
              or a remote path (e.g. to an s3 bucket).
              `loader_fn`: Callable[None, ray.data.Dataset], Instead of
              specifying paths and format, you can specify a function to load the dataset.
              `parallelism`: int, The number of tasks to use for loading the dataset.
              If not specified, it will be set to the number of workers.
              `num_cpus_per_read_task`: float, The number of CPUs to use for each read
              task. If not specified, it will be set to 0.5.
  
      Args:
          config: The config dict for the algorithm.
          num_workers: The number of shards to create for remote workers.
  
      Returns:
          dataset: The dataset object.
          shards: A list of dataset shards. For num_workers > 0 the first returned
          shared would be a dummy None shard for local_worker.
      """
      # check input and input config keys
      assert config.input_ == "dataset", (
          f"Must specify config.input_ as 'dataset' if"
          f" calling `get_dataset_and_shards`. Got {config.input_}"
      )
  
      # check input config format
      input_config = config.input_config
      format = input_config.get("format")
  
      supported_fmts = ["json", "parquet"]
      if format is not None and format not in supported_fmts:
          raise ValueError(
              f"Unsupported format {format}. Supported formats are {supported_fmts}"
          )
  
      # check paths and loader_fn since only one of them is required.
      paths = input_config.get("paths")
      loader_fn = input_config.get("loader_fn")
      if loader_fn and (format or paths):
          raise ValueError(
              "When using a `loader_fn`, you cannot specify a `format` or `path`."
          )
  
      # check if at least loader_fn or format + path is specified.
      if not (format and paths) and not loader_fn:
          raise ValueError(
              "Must specify either a `loader_fn` or a `format` and `path` in "
              "`input_config`."
          )
  
      # check paths to be a str or list[str] if not None
      if paths is not None:
          if isinstance(paths, str):
              paths = [paths]
          elif isinstance(paths, list):
              assert isinstance(
                  paths[0], str
              ), "Paths must be a list of path strings."
          else:
              raise ValueError(
                  "Paths must be a path string or a list of path strings."
              )
          paths = _unzip_if_needed(paths, format)
  
      # TODO (Kourosh): num_workers is not necessary since we can use parallelism for
      # everything. Having two parameters is confusing here. Remove num_workers later.
      parallelism = input_config.get("parallelism", num_workers or 1)
      cpus_per_task = input_config.get(
          "num_cpus_per_read_task", DEFAULT_NUM_CPUS_PER_TASK
      )
  
      if loader_fn:
          dataset = loader_fn()
      elif format == "json":
          dataset = ray.data.read_json(
              paths,
              parallelism=parallelism,
              ray_remote_args={"num_cpus": cpus_per_task},
          )
      elif format == "parquet":
          dataset = ray.data.read_parquet(
              paths,
              parallelism=parallelism,
              ray_remote_args={"num_cpus": cpus_per_task},
          )
      else:
          raise ValueError("Un-supported Ray dataset format: ", format)
  
      # Local worker will be responsible for sampling.
      if num_workers == 0:
          # Dataset is the only shard we need.
          return dataset, [dataset]
      # Remote workers are responsible for sampling:
      else:
          # Each remote worker gets 1 shard.
          remote_shards = dataset.repartition(
              num_blocks=num_workers, shuffle=False
          ).split(num_workers)
  
          # The first None shard is for the local worker, which
          # shouldn't be doing rollout work anyways.
          return dataset, [None] + remote_shards
  
imports: import logging
  import math
  from pathlib import Path
  import re
  import numpy
  from typing import List, Tuple, TYPE_CHECKING, Optional
  import zipfile
  import ray.data
  from ray.rllib.offline.input_reader import InputReader
  from ray.rllib.offline.io_context import IOContext
  from ray.rllib.offline.json_reader import from_json_data, postprocess_actions
  from ray.rllib.policy.sample_batch import concat_samples, SampleBatch, DEFAULT_POLICY_ID
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.typing import SampleBatchType
label_desc: Use image node_id 3 for calling a global function with example usage: get_dataset_and_shards(config, num_workers) and returns: dataset,  list, dataset,  unknown