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: Initialize FASTQLoader.
  
  Parameters
  ----------
  featurizer: Featurizer (default: None)
      The Featurizer to be used for the loaded FASTQ data.
      The featurizer is initialized as a OneHotFeaturizer object with charset ("A", "C", "T", "G") and
      max_length = None.
  auto_add_annotations: bool (default False)
      Whether create_dataset will automatically add [CLS] and [SEP] annotations
      to the sequences it reads in order to assist tokenization.
      Keep False if your FASTQ file already includes [CLS] and [SEP] annotations.
  return_quality_scores: bool (default True)
      returns the quality (likelihood) score of the nucleotides in the sequence.
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code: def __init__(
      self,
      featurizer: Optional[Featurizer] = None,
      auto_add_annotations: bool = False,
      return_quality_scores: bool = False,
  ):
      """Initialize FASTQLoader.
  
      Parameters
      ----------
      featurizer: Featurizer (default: None)
          The Featurizer to be used for the loaded FASTQ data.
          The featurizer is initialized as a OneHotFeaturizer object with charset ("A", "C", "T", "G") and
          max_length = None.
      auto_add_annotations: bool (default False)
          Whether create_dataset will automatically add [CLS] and [SEP] annotations
          to the sequences it reads in order to assist tokenization.
          Keep False if your FASTQ file already includes [CLS] and [SEP] annotations.
      return_quality_scores: bool (default True)
          returns the quality (likelihood) score of the nucleotides in the sequence.
      """
  
      # Set attributes
      self.auto_add_annotations = auto_add_annotations
  
      self.user_specified_features = None
  
      # Handle special featurizer cases
      if isinstance(
          featurizer, UserDefinedFeaturizer
      ):  # User defined featurizer
          self.user_specified_features = featurizer.feature_fields
      elif featurizer is None:  # Default featurizer
          featurizer = OneHotFeaturizer(
              charset=["A", "C", "T", "G"], max_length=None
          )
  
      # Set self.featurizer
      self.featurizer = featurizer
      # Set self.return_quality_scores
      self.return_quality_scores = return_quality_scores
  
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 1 to create a new FASTQLoader object from inherited base classes: DataLoader with example: obj = FASTQLoader(featurizer, auto_add_annotations, return_quality_scores)