class_docstr: Wraps another dataset reader and uses it to read from multiple input files.
  
  Note that in this case the `file_path` passed to `read()` should either be a glob path
  or a path or URL to an archive file ('.zip' or '.tar.gz').
  
  The dataset reader will return instances from all files matching the glob, or all
  files within the archive.
  
  The order the files are processed in is deterministic to enable the
  instances to be filtered according to worker rank in the distributed training or multi-process
  data loading scenarios. In either case, the number of file shards should ideally be a multiple
  of the number of workers, and each file should produce roughly the same number of instances.
  
  Registered as a `DatasetReader` with name "sharded".
  
  # Parameters
  
  base_reader : `DatasetReader`
      Reader with a read method that accepts a single file.
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docstr: Just delegate to the base reader text_to_instance.
docstr_tok: ["Just","delegate","to","the","base","reader","text_to_instance","."]
code: def text_to_instance(self, *args, **kwargs) -> Instance:
      """
      Just delegate to the base reader text_to_instance.
      """
      return self.reader.text_to_instance(*args, **kwargs)
  
file_path: allennlp/allennlp/data/dataset_readers/sharded_dataset_reader.py
imports: import glob
  import logging
  import os
  from typing import Iterable
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.file_utils import cached_path
  from allennlp.data.dataset_readers.dataset_reader import DatasetReader, PathOrStr
  from allennlp.data.instance import Instance
label_desc: Use image node_id 2 for calling the ShardedDatasetReader obj's underlying member method code with example usage: obj.text_to_instance() and returns: self