: device_(device),
        tensors_(std::move(tensors)),
        shape_(std::vector<int64_t>(shape.begin(), shape.end())),
        dtype_(dtype) {}
  ParallelTensor(const ParallelDevice& device,
                 std::vector<TensorHandlePtr> tensors, const TF_DataType dtype)
      : device_(device),
        tensors_(std::move(tensors)),
        shape_(absl::nullopt),
        dtype_(dtype) {}

  if (av_tensor == nullptr) {
    TF_ASSIGN_OR_RETURN(
        *tensor, MakeTensorFromPjRtBuffer(tensor->dtype(), tensor->shape(),
                                          std::move(buffer)));
  } else {
    av_tensor->SetBuffer(std::move(buffer));
  }
  return absl::OkStatus();
}

absl::StatusOr<xla::PjRtCApiClient*> GetPjRtCApiClient(
    const DeviceType& device_type) {

  fun invalidatesCache(method: String): Boolean =
    (
      method == "POST" || method == "PATCH" || method == "PUT" ||
        method == "DELETE" || method == "MOVE"
    )

  @JvmStatic // Despite being 'internal', this method is called by popular 3rd party SDKs.
  fun requiresRequestBody(method: String): Boolean =
    (
      method == "POST" || method == "PUT" ||

    }
  }

  TF_DeleteTensor(analytical_tensor);
  delete[] danalytical;
}

Model BuildGradModel(Model forward, GradientRegistry registry) {
  return [forward_model = std::move(forward),
          grad_registry = std::move(registry)](
             AbstractContext* ctx,
             absl::Span<AbstractTensorHandle* const> inputs,
             absl::Span<AbstractTensorHandle*> outputs) -> Status {

    @Secured({ ROLE, ROLE + VIEW })
    public HtmlResponse move(final ListForm form) {
        saveToken();
        return doMove(form, 0);
    }

    protected HtmlResponse doMove(final ListForm form, final int move) {
        form.initialize();
        Integer pageNumber = form.pn;
        if (pageNumber != null && pageNumber > 0) {
            pageNumber = pageNumber + move;
            if (pageNumber < 1) {

    stack_traces[n->name()] = n->GetStackTrace();
  }

  TF_Function* tf_function = new TF_Function();
  tf_function->record = new tensorflow::FunctionRecord(
      std::move(fdef), std::move(stack_traces), false);

  return tf_function;
}

TF_Function* TF_GraphToFunction(const TF_Graph* fn_body, const char* fn_name,
                                unsigned char append_hash_to_fn_name,

	//		outcode(int($1), &$2, 0, &nullgen);
	//	}
	NOP	$4

	//
	// special
	//
	SYSCALL
	BREAK
	SYNC

	//
	// conditional move on zero/nonzero gp value
	//
	CMOVN	R1, R2, R3
	CMOVZ	R1, R2, R3

	//
	// conditional move on fp false/true
	//
	CMOVF	R1, R2
	CMOVT	R1, R2

	//
	// conditional traps
	//
	TEQ	$1, R1, R2
	TEQ	$1, R1


	//

          {std::move(bucket), std::move(object), &gcs_file->gcs_client,
           TempFile(temp_file_name, std::ios::binary | std::ios::trunc), false,
           static_cast<int64_t>(metadata->size())});
    } else if (TF_GetCode(status) == TF_NOT_FOUND) {
      file->plugin_file = new tf_writable_file::GCSFile(
          {std::move(bucket), std::move(object), &gcs_file->gcs_client,

set -euxo pipefail

cd "$TFCI_OUTPUT_DIR"

# Move extra wheel files somewhere out of the way. This script
# expects just one wheel file to exist.
if [[ "$(ls *.whl | wc -l | tr -d ' ')" != "1" ]]; then
  echo "More than one wheel file is present: moving the oldest to"
  echo "$TFCI_OUTPUT_DIR/extra_wheels."
  # List all .whl files by their modification time (ls -t) and move anything

                                    // ignore
                                }
                                Files.move(path, newPath);
                                logger.info("Move {} to {}", path, newPath);
                            } catch (final IOException e) {
                                logger.warn("Failed to move {}", path, e);
                            }
                        }
                    }
                });