*  `tf.Tensor`

TFE_TensorHandle* CopyToParallelDevice(TFE_Context* context,
                                       TFE_TensorHandle* tensor,
                                       TF_Status* status, void* device_info) {
  TF_SetStatus(
      status, TF_UNIMPLEMENTED,
      absl::StrCat("Trying to copy a tensor ",
                   tensorflow::unwrap(tensor)->DebugString(),
                   " on to a parallel device. Pack non-parallel "

  ASSERT_EQ(outputs->size(), 1);
  ParallelTensor* parallel_result = (*outputs)[0].get();
  ExpectScalarEq<float>(parallel_result->tensor(0), 3.);
  ExpectScalarEq<float>(parallel_result->tensor(1), 3.);

  run_collective = parallel_device.ScalarsFromSequence<bool>(
      {true, false}, context.get(), status.get());
  parallel_device.Execute(context.get(),

### Classes como tipos

Você também pode declarar uma classe como o tipo de uma variável.

Digamos que você tenha uma classe `Person`, com um nome:

```Python hl_lines="1-3"
{!../../docs_src/python_types/tutorial010.py!}
```

Então você pode declarar que uma variável é do tipo `Person`:

```Python hl_lines="6"
{!../../docs_src/python_types/tutorial010.py!}
```

                                                       TF_Status* status) const;

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(
      absl::Span<const DataType> values, TFE_Context* context,
      TF_Status* status) const;

    if (localValues != null) {
      set(combine(localValues));
    }
  }

  @Override
  void releaseResources(ReleaseResourcesReason reason) {
    super.releaseResources(reason);
    this.values = null;
  }

  abstract C combine(List<@Nullable Present<V>> values);

  /** Used for {@link Futures#allAsList} and {@link Futures#successfulAsList}. */

        super("The POM expression: " + expression + " could not be evaluated. Reason: " + message, cause);

        this.expression = expression;
        this.originalMessage = message;
    }

    public ModelInterpolationException(String expression, String message) {
        super("The POM expression: " + expression + " could not be evaluated. Reason: " + message);

        this.expression = expression;

            return result;
        } catch (final ExpressionEvaluationException e) {
            final String reason = String.format(
                    "Cannot evaluate expression '%s' for configuration entry '%s'", value, configuration.getName());

            throw new ComponentConfigurationException(configuration, reason, e);
        }
    }

    public Object fromConfiguration(
            final ConverterLookup lookup,

		// have StatusCode to help to convert to object errors.
		return err
	}

	if len(googleAPIErr.Errors) == 0 {
		return err
	}

	reason := googleAPIErr.Errors[0].Reason
	message := googleAPIErr.Errors[0].Message

	switch reason {
	case "required":
		// Anonymous users does not have storage.xyz access to project 123.
		fallthrough
	case "keyInvalid":
		fallthrough
	case "forbidden":

    TFE_Context* context, TFE_TensorHandle* tensor, TF_Status* status) const {
  std::vector<TensorHandlePtr> components;
  components.reserve(underlying_devices_.size());
  for (const std::string& underlying_device_name : underlying_devices_) {
    TFE_TensorHandle* t = TFE_TensorHandleCopyToDevice(
        tensor, context, underlying_device_name.c_str(), status);