*/
    public Set<String> getRunningSessionIdSet() {
        return runningProcessMap.keySet();
    }

    /**
     * Sets the timeout for process destruction.
     *
     * @param processDestroyTimeout timeout in seconds for process destruction
     */
    public void setProcessDestroyTimeout(final int processDestroyTimeout) {
        this.processDestroyTimeout = processDestroyTimeout;
    }

    /**

  typedef void (*MemoryReleaser)(void* data, size_t len, void* arg);

  // Create a tensor instance from the given data buffer and description.
  // `memory_releaser` will be called on destruction, and it's responsible for
  // cleaning up the underlying buffer.
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, const int64_t* dims, int num_dims, void* data, size_t len,

            // Wait a bit for the processes to start
            Thread.sleep(50);

            // Destroy all processes
            processHelper.destroy();

            // Wait a bit for destruction to complete
            Thread.sleep(100);

            assertFalse(processHelper.isProcessRunning());
        } catch (Exception e) {
            fail("Unexpected exception: " + e.getMessage());
        }

namespace tensorflow {

namespace {

// Managing context for the DLManagedTensor, will manage the lifetime of
// DLManagedTensor. When calling DLManagedTensor::deleter, it will notify the
// original framework of destruction, and this context will be deleted also.
struct TfDlManagedTensorCtx {
  TensorReference reference;
  std::vector<int64_t> shape;
  std::vector<int64_t> strides;
  DLManagedTensor tensor;

	go f()
}
</pre>

<p>
calling <code>hello</code> will print <code>"hello, world"</code>
at some point in the future (perhaps after <code>hello</code> has returned).
</p>

<h3 id="goexit">Goroutine destruction</h3>

<p>
The exit of a goroutine is not guaranteed to be synchronized before
any event in the program.
For example, in this program:
</p>

<pre>
var a string

func hello() {

        List<Restriction> restrictions = range.getRestrictions();
        assertEquals(1, restrictions.size(), CHECK_NUM_RESTRICTIONS);
        Restriction restriction = restrictions.get(0);
        assertNull(restriction.getLowerBound(), CHECK_LOWER_BOUND);
        assertFalse(restriction.isLowerBoundInclusive(), CHECK_LOWER_BOUND_INCLUSIVE);
        assertEquals("1.0", restriction.getUpperBound().toString(), CHECK_UPPER_BOUND);

	MOVQ CR8, (AX)                  // ERROR "invalid instruction"
	MOVQ (AX), CR0                  // ERROR "invalid instruction"
	MOVQ (AX), CR2                  // ERROR "invalid instruction"
	MOVQ (AX), CR3                  // ERROR "invalid instruction"
	MOVQ (AX), CR4                  // ERROR "invalid instruction"
	MOVQ (AX), CR8                  // ERROR "invalid instruction"

    }

    private List<Restriction> intersection(List<Restriction> r1, List<Restriction> r2) {
        List<Restriction> restrictions = new ArrayList<>(r1.size() + r2.size());
        Iterator<Restriction> i1 = r1.iterator();
        Iterator<Restriction> i2 = r2.iterator();
        Restriction res1 = i1.next();
        Restriction res2 = i2.next();

        boolean done = false;

   
   
   *TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.

	}
	return false
}

// IsRISCV64VTypeI reports whether op is a vtype immediate instruction that
// requires special handling.
func IsRISCV64VTypeI(op obj.As) bool {
	return op == riscv.AVSETVLI || op == riscv.AVSETIVLI
}

// IsRISCV64CSRO reports whether the op is an instruction that uses
// CSR symbolic names and whether that instruction expects a register
// or an immediate source operand.