class_docstr: Mask out values in a 1-dimensional buffer using a fixed masking pattern.
class_docstr_tok: ["Mask","out","values","in","a","1-dimensional","buffer","using","a","fixed","masking","pattern","."]
code: def _generate_masks(self, N: int) -> torch.Tensor:
      if self._cached_masks is None:
          g = torch.Generator()
          # We want to fix the mask across all inputs, so we fix the seed.
          # Choose a seed with a good balance of 0 and 1 bits. See:
          # https://pytorch.org/docs/stable/generated/torch.Generator.html#torch.Generator.manual_seed
          g.manual_seed(2147483647)
          random_mask = torch.zeros(
              [N], requires_grad=False, dtype=torch.bool
          )
          random_mask[
              torch.randperm(N, generator=g)[: int(self.keep_frac * N)]
          ] = 1
          self._cached_masks = random_mask
      return self._cached_masks
  
imports: import argparse
  import io
  from typing import Dict, Union
  import numpy
  import torch
  from PIL import Image
  from data.transforms import TRANSFORMATIONS_REGISTRY, BaseTransformation
label_desc: Use image node_id 2 for calling the MaskPositions obj's underlying member method code with example usage: obj._generate_masks(N) and returns: self