class_docstr: Reader object that loads the dataset from the D4RL dataset.
class_docstr_tok: ["Reader","object","that","loads","the","dataset","from","the","D4RL","dataset","."]
docstr: Initializes a D4RLReader instance.
  
  Args:
      inputs: String corresponding to the D4RL environment name.
      ioctx: Current IO context object.
docstr_tok: ["Initializes","a","D4RLReader","instance",".","Args",":","inputs",":","String","corresponding","to","the","D4RL","environment","name",".","ioctx",":","Current","IO","context","object","."]
code: def __init__(self, inputs: str, ioctx: IOContext = None):
      """Initializes a D4RLReader instance.
  
      Args:
          inputs: String corresponding to the D4RL environment name.
          ioctx: Current IO context object.
      """
      import d4rl
  
      self.env = gym.make(inputs)
      self.dataset = _convert_to_batch(d4rl.qlearning_dataset(self.env))
      assert self.dataset.count >= 1
      self.counter = 0
  
file_path: ray/python/ray/rllib/offline/d4rl_reader.py
imports: import logging
  import gymnasium
  from ray.rllib.offline.input_reader import InputReader
  from ray.rllib.offline.io_context import IOContext
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.typing import SampleBatchType
  from typing import Dict
label_desc: Use image node_id 1 to create a new D4RLReader object from inherited base classes: InputReader with example: obj = D4RLReader(inputs, ioctx)