code: def record(train_mode=True):  # pylint: disable=redefined-outer-name
      """Returns an autograd recording scope context to be used in 'with' statement
      and captures code that needs gradients to be calculated.
  
      .. note:: When forwarding with train_mode=False, the corresponding backward
                should also use train_mode=False, otherwise gradient is undefined.
  
      Example::
  
          with autograd.record():
              y = model(x)
              backward([y])
          metric.update(...)
          optim.step(...)
  
      Parameters
      ----------
      train_mode: bool, default True
          Whether the forward pass is in training or predicting mode. This controls the behavior
          of some layers such as Dropout, BatchNorm.
      """
      return _RecordingStateScope(True, train_mode)
  
imports: from array import array
  from threading import Lock
  import traceback
  import ctypes
  from ctypes import c_int, c_void_p, CFUNCTYPE, POINTER, cast
  from .base import _LIB, check_call, string_types, mx_uint
  from .base import NDArrayHandle, c_array, c_handle_array, c_array_buf, MXCallbackList, SymbolHandle
  from .ndarray import NDArray, _ndarray_cls
  from .ndarray import _GRAD_REQ_MAP
  from .symbol import Symbol
  from .util import is_np_array
label_desc: Use image node_id 5 for calling a global function with example usage: record(train_mode) and returns: _RecordingStateScope