code: def GetRandomNormalInput(shape, dtype):
      # float16 has limited range so we reduce the variance of the scalars.
      scale = 10.0 if dtype != np.float16 else 0.1
      loc = -10.0 if dtype != np.float16 else 0.1
      vals = np.array(
          np.random.normal(loc, scale, np.prod(shape)), dtype=dtype
      )
      if dtype in (np.complex64, np.complex128):
          imag = np.array(
              np.random.normal(loc, scale, np.prod(shape)), dtype=dtype
          )
          vals += 1j * imag
      return vals.reshape(shape)
  
imports: import numpy
  from tensorflow.python import tf2
  from tensorflow.python.client import session
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import benchmark
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling a global function with example usage: GetRandomNormalInput(shape, dtype) and returns: vals