Michael Hudgins <******@****.***> 1695048765 +0000

TensorFlower Gardener <******@****.***> 1689191273 -0700

	}

	fd := int(netns.Fd())
	resp, err := latestConn.send(ctx, data, &fd)
	if err != nil {
		return err
	}

	if resp.GetAck().GetError() != "" {
		log.Errorf("add-workload: got ack error: %s", resp.GetAck().GetError())
		return fmt.Errorf("got ack error: %s", resp.GetAck().GetError())
	}
	return nil
}

// TODO ctx is unused here
// nolint: unparam

    "@webpack-cli/serve" "^2.0.3"
    colorette "^2.0.14"
    commander "^10.0.1"
    cross-spawn "^7.0.3"
    envinfo "^7.7.3"
    fastest-levenshtein "^1.0.12"
    import-local "^3.0.2"
    interpret "^3.1.1"
    rechoir "^0.8.0"
    webpack-merge "^5.7.3"

webpack-merge@^4.1.5:
  version "4.2.2"

    // 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)

tfrun bazel $TFCI_BAZEL_BAZELRC_ARGS test $TFCI_BAZEL_COMMON_ARGS --config=linux_libtensorflow_test
tfrun bazel $TFCI_BAZEL_BAZELRC_ARGS build $TFCI_BAZEL_COMMON_ARGS --config=linux_libtensorflow_build

tfrun ./ci/official/utilities/repack_libtensorflow.sh "$TFCI_OUTPUT_DIR" "$TFCI_LIB_SUFFIX"

if [[ "$TFCI_ARTIFACT_STAGING_GCS_ENABLE" == 1 ]]; then
  # Note: -n disables overwriting previously created files.

        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));

    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()),