class_docstr: Handles loading of FASTQ files.
  
  FASTQ files are commonly used to hold very large sequence data. It is a variant of FASTA format.
  This class provides convenience files to load FASTQ data and one-hot encode
  the genomic sequences for use in downstream learning tasks.
  
  Example
  -------
  >>> import os
  >>> from deepchem.feat.molecule_featurizers import OneHotFeaturizer
  >>> from deepchem.data.data_loader import FASTQLoader
  >>> current_dir = os.path.dirname(os.path.abspath(__file__))
  >>> input_file = os.path.join(current_dir, "tests", "sample1.fastq")
  >>> loader = FASTQLoader()
  >>> sequences = loader.create_dataset(input_file)
  
  See Also
  --------
  `Info on the structure of FASTQ files <https://support.illumina.com/bulletins/2016/04/fastq-files-explained.html>`
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docstr: Defines a generator which returns data for each shard
  
  Parameters
  ----------
  input_files: List[str]
      List of file names to process
  n_samples:  int, optional
      The number of samples to extract from each variant in the data
  shard_size: int, optional (default 4096)
      The size of a shard of data to process at a time. Here, shard_size is equal to the
      number of variants to fetch. You can think of them as number of rows to get from the
      full dataset.
  
  Yields
  -------
  Iterator
      Iterator over shards
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code: def _get_shards(
      self, input_files: List[str], shard_size: Optional[int] = 4096
  ) -> Iterator:
      """Defines a generator which returns data for each shard
  
      Parameters
      ----------
      input_files: List[str]
          List of file names to process
      n_samples:  int, optional
          The number of samples to extract from each variant in the data
      shard_size: int, optional (default 4096)
          The size of a shard of data to process at a time. Here, shard_size is equal to the
          number of variants to fetch. You can think of them as number of rows to get from the
          full dataset.
  
      Yields
      -------
      Iterator
          Iterator over shards
      """
  
      return _fastq_load_files(input_files, shard_size)
  
imports: import os
  import tempfile
  import zipfile
  import time
  import logging
  import warnings
  from typing import List, Optional, Tuple, Any, Sequence, Union, Iterator
  import pandas
  import numpy
  from deepchem.utils.typing import OneOrMany
  from deepchem.utils.data_utils import load_image_files, load_csv_files, load_json_files, load_sdf_files, unzip_file
  from deepchem.feat import UserDefinedFeaturizer, Featurizer
  from deepchem.data import Dataset, DiskDataset, NumpyDataset, ImageDataset
  from deepchem.feat.molecule_featurizers import OneHotFeaturizer
  from deepchem.utils.genomics_utils import encode_bio_sequence
label_desc: Use image node_id 2 for calling the FASTQLoader obj's underlying member method code with example usage: obj._get_shards(input_files, shard_size) and returns: _fastq_load_files