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: Realize a squeeze for the dim given for all properties.
docstr_tok: ["Realize","a","squeeze","for","the","dim","given","for","all","properties","."]
code: def squeeze(self, dim: int = 0) -> SegmentationResults:
      """Realize a squeeze for the dim given for all properties."""
      self.logits = self.logits.squeeze(dim)
      self.scores = self.scores.squeeze(dim)
      if isinstance(self._original_res_logits, Tensor):
          self._original_res_logits = self._original_res_logits.squeeze(
              dim
          )
  
      return self
  
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 3 for calling the SegmentationResults obj's underlying member method code with example usage: obj.squeeze(dim) and returns: self