code: def dot_equivalent():
      # MNIST-scale convolution operation
      import autograd.scipy.signal
  
      dat = npr.randn(256, 3, 24, 5, 24, 5)
      kernel = npr.randn(3, 5, 5)
      with tictoc():
          np.tensordot(dat, kernel, axes=[(1, 3, 5), (0, 1, 2)])
  
file_path: autograd/tests/profiling.py
imports: from __future__ import absolute_import
  from __future__ import print_function
  from builtins import range
  from autograd import grad
  import autograd.numpy
  import autograd.numpy.random
  from time import time
  from contextlib import contextmanager
label_desc: Use image node_id 4 for calling a global function with example usage: dot_equivalent() without return types