class_docstr: An error-collecting object which supports error recovery.
  
  It is intended to be used like a context manager:
  
  >>> with Errors("Top-level error message") as errs:
  >>>     ...
class_docstr_tok: ["An","error-collecting","object","which","supports","error","recovery",".","It","is","intended","to","be","used","like","a","context","manager",":",">",">",">","with","Errors","(","``","Top-level","error","message","''",")","as","errs",":",">",">",">","..."]
docstr: Test that x and y are nearly equal (equal within self.rtol
  precision); aborts execution if they are not.
docstr_tok: ["Test","that","x","and","y","are","nearly","equal","(","equal","within","self.rtol","precision",")",";","aborts","execution","if","they","are","not","."]
code: def requireAlmostEqual(self, x, y, msg=None):
      """
      Test that x and y are nearly equal (equal within self.rtol
      precision); aborts execution if they are not.
      """
      self.almostEqualAndThen(x, y, msg, self.failWith)
  
imports: import difflib
  import io
  import numpy
  import onnx
  import onnx.helper
  import torch
  import torch.jit
  import torch.onnx
label_desc: Use image node_id 2 for calling the Errors obj's underlying member method code with example usage: obj.requireAlmostEqual(x, y, msg) without return types