class_docstr: Encapsulate the results obtained by a Segmentation model.
  
  Args:
      logits: Results logits with shape :math:`(B, C, H, W)`, where :math:`C` refers to the number of predicted masks
      scores: The scores from the logits. Shape :math:`(B, C)`
      mask_threshold: The threshold value to generate the `binary_masks` from the `logits`
class_docstr_tok: ["Encapsulate","the","results","obtained","by","a","Segmentation","model",".","Args",":","logits",":","Results","logits","with","shape",":","math",":","`","(","B",",","C",",","H",",","W",")","`",",","where",":","math",":","`","C","`","refers","to","the","number","of","predicted","masks","scores",":","The","scores","from","the","logits",".","Shape",":","math",":","`","(","B",",","C",")","`","mask_threshold",":","The","threshold","value","to","generate","the","`","binary_masks","`","from","the","`","logits","`"]
docstr: Binary mask generated from logits considering the mask_threshold.
  
  Shape will be the same of logits :math:`(B, C, H, W)` where :math:`C` is the number masks predicted.
  
  .. note:: If you run `original_res_logits`, this will generate the masks
     based on the original resolution logits.
     Otherwise, this will use the low resolution logits (self.logits).
docstr_tok: ["Binary","mask","generated","from","logits","considering","the","mask_threshold",".","Shape","will","be","the","same","of","logits",":","math",":","`","(","B",",","C",",","H",",","W",")","`","where",":","math",":","`","C","`","is","the","number","masks","predicted",".","..","note",":",":","If","you","run","`","original_res_logits","`",",","this","will","generate","the","masks","based","on","the","original","resolution","logits",".","Otherwise",",","this","will","use","the","low","resolution","logits","(","self.logits",")","."]
code: def binary_masks(self) -> Tensor:
      """Binary mask generated from logits considering the mask_threshold.
  
      Shape will be the same of logits :math:`(B, C, H, W)` where :math:`C` is the number masks predicted.
  
      .. note:: If you run `original_res_logits`, this will generate the masks
         based on the original resolution logits.
         Otherwise, this will use the low resolution logits (self.logits).
      """
      if self._original_res_logits is not None:
          x = self._original_res_logits
      else:
          x = self.logits
  
      return x > self.mask_threshold
  
file_path: kornia/kornia/contrib/models/structures.py
imports: from __future__ import annotations
  from dataclasses import dataclass
  from typing import Optional
  from kornia.core import Tensor
  from kornia.core.check import KORNIA_CHECK
  from kornia.geometry.transform import resize
label_desc: Use image node_id 1 for calling the SegmentationResults obj's underlying member method code with example usage: obj.binary_masks() and returns: unknown