class_docstr: Image resolution upscaling schedule.
class_docstr_tok: ["Image","resolution","upscaling","schedule","."]
docstr: Initializer.
  
  Args:
    scale_mode: 'ALL' (along both H and W) or 'H' (along H).
    start_resolutions: An tuple of integers of HxW format for start image
    resolutions. Defaults to (4, 4).
    scale_base: An integer of resolution base multiplier. Defaults to 2.
    num_resolutions: An integer of how many progressive resolutions (including
        `start_resolutions`). Defaults to 4.
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code: def __init__(
      self,
      scale_mode="ALL",
      start_resolutions=(4, 4),
      scale_base=2,
      num_resolutions=4,
  ):
      """Initializer.
  
      Args:
        scale_mode: 'ALL' (along both H and W) or 'H' (along H).
        start_resolutions: An tuple of integers of HxW format for start image
        resolutions. Defaults to (4, 4).
        scale_base: An integer of resolution base multiplier. Defaults to 2.
        num_resolutions: An integer of how many progressive resolutions (including
            `start_resolutions`). Defaults to 4.
      """
      self._scale_mode = scale_mode
      self._start_resolutions = start_resolutions
      self._scale_base = scale_base
      self._num_resolutions = num_resolutions
  
imports: import math
  from magenta.models.gansynth.lib import layers
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 1 to create a new ResolutionSchedule object from inherited base classes: object with example: obj = ResolutionSchedule(scale_mode, start_resolutions, scale_base, num_resolutions)