For a more "real life" example of this, imagine a bank.

Up to recently, most of the banks had multiple cashiers 👨‍💼👨‍💼👨‍💼👨‍💼 and a big line 🕙🕙🕙🕙🕙🕙🕙🕙.

All of the cashiers doing all the work with one client after the other 👨‍💼⏯.

And you have to wait 🕙 in the line for a long time or you lose your turn.

You probably wouldn't want to take your crush 😍 with you to do errands at the bank 🏦.

### Burger Conclusion

  By the time she had caught the flamingo and brought it back,
the fight was over, and both the hedgehogs were out of sight:
`but it doesn't matter much,' thought Alice, `as all the arches
are gone from this side of the ground.'  So she tucked it away
under her arm, that it might not escape again, and went back for
a little more conversation with her friend.

  When she got back to the Cheshire Cat, she was surprised to

    // Write the mocking script.
    peer.sendFrame().settings(Settings())
    peer.acceptFrame() // ACK
    peer.sendFrame().ping(false, 2, 3)
    peer.acceptFrame() // PING
    peer.play()

    // Play it back.
    connect(peer)

    // Verify the peer received what was expected.
    val ping = peer.takeFrame()
    assertThat(ping.type).isEqualTo(Http2.TYPE_PING)
    assertThat(ping.streamId).isEqualTo(0)

  By the time she had caught the flamingo and brought it back,
the fight was over, and both the hedgehogs were out of sight:
`but it doesn't matter much,' thought Alice, `as all the arches
are gone from this side of the ground.'  So she tucked it away
under her arm, that it might not escape again, and went back for
a little more conversation with her friend.

  When she got back to the Cheshire Cat, she was surprised to

    *   XLA reduction emitter is deterministic when the environment variable
        `TF_DETERMINISTIC_OPS` is set to "true" or "1". This extends
        deterministic `tf.nn.bias_add` back-prop functionality (and therefore
        also deterministic back-prop of bias-addition in Keras layers) to
        include when XLA JIT compilation is enabled.
    *   Fix problem, when running on a CUDA GPU and when either environment

        return result;
      }
      components.emplace_back(TFE_TensorHandleCopySharingTensor(
          absl::get<TFE_TensorHandle*>(inputs[i]), status));
    }
    std::vector<MaybeParallelTensorOwned> result_content;
    result_content.reserve(1);
    result_content.push_back(ParallelTensor::FromTensorHandles(
        parallel_device, std::move(components), status));

<c:if test="${crudMode == 1}">
	<button type="submit" class="btn btn-default" name="list"
		value="<la:message key="labels.crud_button_back" />">
		<em class="fa fa-arrow-circle-left">
		<la:message key="labels.crud_button_back" />
	</button>
	<c:if test="${editable}">
	<button type="submit" class="btn btn-success" name="create"
		value="<la:message key="labels.crud_button_create" />">

    for (int i = 0; i < noutputs; ++i) {
      output_names_vec.push_back(string(output_names[i]));
    }
  }

  // Process control output names.
  std::vector<string> control_output_names_vec;
  if (control_output_names) {
    control_output_names_vec.reserve(ncontrol_outputs);
    for (int i = 0; i < ncontrol_outputs; ++i) {
      control_output_names_vec.push_back(string(control_output_names[i]));
    }
  }

    components.reserve(underlying_devices_.size());
    for (int j = 0; j < underlying_devices_.size(); ++j) {
      components.push_back(std::move(per_device_output_tensors[j][i]));
    }
    if (expected_output_shapes[i].IsFullyDefined()) {
      per_device_outputs.push_back(ParallelTensor::FromTensorHandles(
          *this, std::move(components),
          absl::Span<const int64_t>(expected_output_shapes[i].dim_sizes()),

        // GradientTape which uses ones.
        Gradient* zero = input_tensors[target_index].ZerosLike();
        new_zeros.push_back(zero);
        in_grads.push_back(zero);
      } else {
        in_grads.push_back(nullptr);
      }
    } else {
      in_grads.push_back(current_grad->second);
    }
  }

  // Avoid infinite recursion. Whichever forward function we run, it'll end up