*   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

    // 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)

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

    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]));
    }
  }

        // 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

    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()),

    items = relationship("Item", back_populates="owner")


class Item(Base):
    __tablename__ = "items"

    id = Column(Integer, primary_key=True)
    title = Column(String, index=True)
    description = Column(String, index=True)
    owner_id = Column(Integer, ForeignKey("users.id"))

    items = relationship("Item", back_populates="owner")


class Item(Base):
    __tablename__ = "items"

    id = Column(Integer, primary_key=True)
    title = Column(String, index=True)
    description = Column(String, index=True)
    owner_id = Column(Integer, ForeignKey("users.id"))

        assertEquals( Artifact.SCOPE_COMPILE, artifact.getScope(), "dependency artifact has wrong scope." );

        //check for back-propagation of default scope.
        assertEquals( Artifact.SCOPE_COMPILE, dep.getScope(), "default scope NOT back-propagated to dependency." );
        */
    }

    @Test
    @Disabled
    void testShouldUseInjectedTestScopeFromDependencyManagement() throws Exception {

		defer threadNS.Close()

		// switch to target namespace
		if err = NetnsSet(fdable); err != nil {
			return err
		}
		defer func() {
			err := threadNS.Set() // switch back
			if err == nil {
				// Unlock the current thread only when we successfully switched back
				// to the original namespace; otherwise leave the thread locked which
				// will force the runtime to scrap the current thread, that is maybe