class_docstr: Control automatic evaluation
  
  Explanation
  ===========
  
  This context manager controls whether or not all SymPy functions evaluate
  by default.
  
  Note that much of SymPy expects evaluated expressions.  This functionality
  is experimental and is unlikely to function as intended on large
  expressions.
  
  Examples
  ========
  
  >>> from sympy import evaluate
  >>> from sympy.abc import x
  >>> print(x + x)
  2*x
  >>> with evaluate(False):
  ...     print(x + x)
  x + x
class_docstr_tok: ["Control","automatic","evaluation","Explanation","===========","This","context","manager","controls","whether","or","not","all","SymPy","functions","evaluate","by","default",".","Note","that","much","of","SymPy","expects","evaluated","expressions",".","This","functionality","is","experimental","and","is","unlikely","to","function","as","intended","on","large","expressions",".","Examples","========",">",">",">","from","sympy","import","evaluate",">",">",">","from","sympy.abc","import","x",">",">",">","print","(","x","+","x",")","2","*","x",">",">",">","with","evaluate","(","False",")",":","...","print","(","x","+","x",")","x","+","x"]
code: def __init__(self, x):
      self.x = x
      self.old = []
  
file_path: sympy/sympy/core/parameters.py
imports: from .cache import clear_cache
  from contextlib import contextmanager
  from threading import local
label_desc: Use image node_id 1 to create a new evaluate object  with example: obj = evaluate(x)