code: def test_cast_float16(self):
      f16 = theano.tensor.vector(dtype="float16")
      f32 = theano.tensor.fvector()
      i8 = theano.tensor.bvector()
      f = theano.function(
          [f16, f32, i8],
          [
              f16.astype("float32"),
              f32.astype("float16"),
              f32.astype("float64"),
              f16.astype("int8"),
              f32.astype("int8"),
              i8.astype("float16"),
              i8.astype("float32"),
          ],
          mode=mode_with_gpu,
      )
  
      d1 = (np.random.rand(4) * 10).astype("float16")
      d2 = (np.random.rand(5) * 10).astype("float32")
      d3 = (np.random.rand(6) * 10).astype("int8")
      res = f(d1, d2, d3)
  
      for i, out in enumerate(f.outputs):
          dtype = out.variable.dtype
          assert res[i].dtype == dtype
          inp = out.variable.owner.inputs[0]
          if inp.dtype == "float16":
              d = d1
          elif inp.dtype == "float32":
              d = d2
          else:
              d = d3
          assert_allclose(d.astype(dtype), res[i])
  
imports: from __future__ import absolute_import, print_function, division
  from copy import copy
  from unittest import TestCase
  import numpy
  import theano
  from theano import scalar, gof, tensor
  from theano.compile import DebugMode, Mode
  from theano.tests.unittest_tools import SkipTest, assert_allclose
  from theano.tensor.tests import test_elemwise
  from .config import mode_with_gpu, mode_without_gpu, test_ctx_name
  from .test_basic_ops import rand_gpuarray
  from ..elemwise import GpuElemwise, GpuDimShuffle, GpuCAReduceCuda, GpuCAReduceCPY, GpuErfinv, GpuErfcinv
  from ..dnn import GpuDnnReduction
  from ..type import GpuArrayType, get_context, gpuarray_shared_constructor
  from pygpu import ndgpuarray
label_desc: Use image node_id 2 for calling the test_float16 obj's underlying member method code with example usage: obj.test_cast_float16() without return types