class_docstr: Spatial Video Mask
  
  Will mask videos frames in the spatial dimensions using horizontal and vertical masks
  
  params: 
      num_horizontal_masks: number of horizontal masks
      num_vertical_masks: number of vertical masks
      max_h: maximum width of horizontal mask  
      max_v: maximum width of vertical mask
      mean_frame: mask using video mean instead of zeros
class_docstr_tok: ["Spatial","Video","Mask","Will","mask","videos","frames","in","the","spatial","dimensions","using","horizontal","and","vertical","masks","params",":","num_horizontal_masks",":","number","of","horizontal","masks","num_vertical_masks",":","number","of","vertical","masks","max_h",":","maximum","width","of","horizontal","mask","max_v",":","maximum","width","of","vertical","mask","mean_frame",":","mask","using","video","mean","instead","of","zeros"]
code: def __init__(
      self,
      num_horizontal_masks=1,
      num_vertical_masks=1,
      max_h=30,
      max_v=30,
      mean_frame=True,
  ):
      super().__init__()
  
      self.num_horizontal_masks = num_horizontal_masks
      self.num_vertical_masks = num_vertical_masks
      self.max_h = max_h
      self.max_v = max_v
      self.mean_frame = mean_frame
      self.random = random.Random()
  
file_path: NeMo/nemo/collections/multimodal/speech_cv/modules/video_augment.py
imports: import random
  from collections import OrderedDict
  import torch
  from torch import nn
  from nemo.core.classes.module import NeuralModule
  from nemo.core.neural_types import NeuralType, VideoSignal
label_desc: Use image node_id 1 to create a new SpatialVideoMasking object from inherited base classes: NeuralModule with example: obj = SpatialVideoMasking(num_horizontal_masks, num_vertical_masks, max_h, max_v, mean_frame)