class_docstr: Support for pyAV as backend.
  
  Parameters
  ----------
  request : iio.Request
      A request object that represents the users intent. It provides a
      standard interface to access various the various ImageResources and
      serves them to the plugin as a file object (or file). Check the docs for
      details.
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docstr: Yield frames from the video.
  
  Parameters
  ----------
  frame : np.ndarray
      A numpy array containing loaded frame data.
  format : str
      Convert the data into the given format before returning it. If None,
      return the data in the encoded format if it can be expressed as a
      strided array; otherwise raise an Exception.
  filter_sequence : List[str, str, dict]
      Set the returned colorspace. If not None (default: rgb24), convert
      the data into the given format before returning it. If ``None``
      return the data in the encoded format if it can be expressed as a
      strided array; otherwise raise an Exception.
  filter_graph : (dict, List)
      If not None, apply the given graph of FFmpeg filters to each
      ndimage. The graph is given as a tuple of two dicts. The first dict
      contains a (named) set of nodes, and the second dict contains a set
      of edges between nodes of the previous dict. Check the (module-level)
      plugin docs for details and examples.
  thread_count : int
      How many threads to use when decoding a frame. The default is 0,
      which will set the number using ffmpeg's default, which is based on
      the codec, number of available cores, threadding model, and other
      considerations.
  thread_type : str
      The threading model to be used. One of
  
      - `"SLICE"` (default): threads assemble parts of the current frame
      - `"FRAME"`: threads may assemble future frames (faster for bulk reading)
  
  
  Yields
  ------
  frame : np.ndarray
      A (decoded) video frame.
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code: def iter(
      self,
      *,
      format: str = "rgb24",
      filter_sequence: List[Tuple[str, Union[str, dict]]] = None,
      filter_graph: Tuple[dict, List] = None,
      thread_count: int = 0,
      thread_type: str = None,
  ) -> np.ndarray:
      """Yield frames from the video.
  
      Parameters
      ----------
      frame : np.ndarray
          A numpy array containing loaded frame data.
      format : str
          Convert the data into the given format before returning it. If None,
          return the data in the encoded format if it can be expressed as a
          strided array; otherwise raise an Exception.
      filter_sequence : List[str, str, dict]
          Set the returned colorspace. If not None (default: rgb24), convert
          the data into the given format before returning it. If ``None``
          return the data in the encoded format if it can be expressed as a
          strided array; otherwise raise an Exception.
      filter_graph : (dict, List)
          If not None, apply the given graph of FFmpeg filters to each
          ndimage. The graph is given as a tuple of two dicts. The first dict
          contains a (named) set of nodes, and the second dict contains a set
          of edges between nodes of the previous dict. Check the (module-level)
          plugin docs for details and examples.
      thread_count : int
          How many threads to use when decoding a frame. The default is 0,
          which will set the number using ffmpeg's default, which is based on
          the codec, number of available cores, threadding model, and other
          considerations.
      thread_type : str
          The threading model to be used. One of
  
          - `"SLICE"` (default): threads assemble parts of the current frame
          - `"FRAME"`: threads may assemble future frames (faster for bulk reading)
  
  
      Yields
      ------
      frame : np.ndarray
          A (decoded) video frame.
  
  
      """
  
      self._video_stream.thread_type = thread_type or "SLICE"
      self._video_stream.codec_context.thread_count = thread_count
  
      self.set_video_filter(filter_sequence, filter_graph)
  
      for frame in self._decoder:
          self._next_idx += 1
  
          if self._video_filter is not None:
              try:
                  frame = self._video_filter.send(frame)
              except StopIteration:
                  break
  
          if frame is None:
              continue
  
          yield self._unpack_frame(frame, format=format)
  
      if self._video_filter is not None:
          for frame in self._video_filter:
              yield self._unpack_frame(frame, format=format)
  
imports: from fractions import Fraction
  from math import ceil
  from typing import Any, Dict, List, Optional, Tuple, Union, Generator
  import av
  import av.filter
  import numpy
  from numpy.lib.stride_tricks import as_strided
  from ..core import Request
  from ..core.request import URI_BYTES, InitializationError, IOMode
  from ..core.v3_plugin_api import ImageProperties, PluginV3
label_desc: Use image node_id 3 for calling the PyAVPlugin obj's underlying member method code with example usage: obj.iter() without return types