class_docstr: The base class of all passes. All methods here are just simple wrappers
  that are implemented in the backend. They are defined for users to
  conveniently interact with the base class.
class_docstr_tok: ["The","base","class","of","all","passes",".","All","methods","here","are","just","simple","wrappers","that","are","implemented","in","the","backend",".","They","are","defined","for","users","to","conveniently","interact","with","the","base","class","."]
docstr: Execute the pass. Note that for sequential pass, the dependency among
  different passes will be resolved in the backend.
  
  Parameters
  ----------
  mod : tvm.IRModule
      The module that a certain optimization is performed on.
  
  Returns
  -------
  mod : tvm.IRModule
      The updated module after applying this pass.
docstr_tok: ["Execute","the","pass",".","Note","that","for","sequential","pass",",","the","dependency","among","different","passes","will","be","resolved","in","the","backend",".","Parameters","--","--","--","--","--","mod",":","tvm.IRModule","The","module","that","a","certain","optimization","is","performed","on",".","Returns","--","--","--","-","mod",":","tvm.IRModule","The","updated","module","after","applying","this","pass","."]
code: def __call__(self, mod):
      """Execute the pass. Note that for sequential pass, the dependency among
      different passes will be resolved in the backend.
  
      Parameters
      ----------
      mod : tvm.IRModule
          The module that a certain optimization is performed on.
  
      Returns
      -------
      mod : tvm.IRModule
          The updated module after applying this pass.
      """
      return _ffi_transform_api.RunPass(self, mod)
  
imports: import inspect
  import functools
  import tvm._ffi
  import tvm.runtime
  from .None import _ffi_transform_api
label_desc: Use image node_id 2 for calling the Pass obj's underlying member method code with example usage: obj.__call__(mod) and returns: _ffi_transform_api