code: def __init__(self, maxlen: int, N: int):
      # we use NaN as an indicator for missing data
      self.data = np.full((maxlen, N), np.nan)
      self.next = 0
      # used to infer the correct framework because this class uses NumPy
      self.tensor: Optional[ep.Tensor] = None
  
imports: from typing import Union, Tuple, Optional, Any
  from typing_extensions import Literal
  import numpy
  import eagerpy
  import logging
  from ..devutils import flatten
  from ..devutils import atleast_kd
  from ..types import Bounds
  from ..models import Model
  from ..criteria import Criterion
  from ..distances import l2
  from ..tensorboard import TensorBoard
  from .blended_noise import LinearSearchBlendedUniformNoiseAttack
  from .base import MinimizationAttack
  from .base import T
  from .base import get_criterion
  from .base import get_is_adversarial
  from .base import raise_if_kwargs
  from .base import verify_input_bounds
label_desc: Use image node_id 1 to create a new ArrayQueue object  with example: obj = ArrayQueue(maxlen, N)