class_docstr: Class for loading data.
code: def __init__(
      self,
      strokes,
      batch_size=100,
      max_seq_length=250,
      scale_factor=1.0,
      random_scale_factor=0.0,
      augment_stroke_prob=0.0,
      limit=1000,
  ):
      self.batch_size = batch_size  # minibatch size
      self.max_seq_length = max_seq_length  # N_max in sketch-rnn paper
      self.scale_factor = scale_factor  # divide offsets by this factor
      self.random_scale_factor = (
          random_scale_factor  # data augmentation method
      )
      # Removes large gaps in the data. x and y offsets are clamped to have
      # absolute value no greater than this limit.
      self.limit = limit
      self.augment_stroke_prob = (
          augment_stroke_prob  # data augmentation method
      )
      self.start_stroke_token = [
          0,
          0,
          1,
          0,
          0,
      ]  # S_0 in sketch-rnn paper
      # sets self.strokes (list of ndarrays, one per sketch, in stroke-3 format,
      # sorted by size)
      self.preprocess(strokes)
  
imports: import random
  import numpy
label_desc: Use image node_id 1 to create a new DataLoader object from inherited base classes: object with example: obj = DataLoader(strokes, batch_size, max_seq_length, scale_factor, random_scale_factor, augment_stroke_prob, limit)