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,

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() {

            return true;
        }

        if (!(other instanceof Restriction)) {
            return false;
        }

        Restriction restriction = (Restriction) other;
        if (lowerBound != null) {
            if (!lowerBound.equals(restriction.lowerBound)) {
                return false;
            }
        } else if (restriction.lowerBound != null) {
            return false;
        }

	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"

        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);

    }

    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.

                                                             TF_Status* status,
                                                             TFE_Context* ctx);

// Calls the destructor of DLManagedTensor, used in the destructor of PyCapsule.
TF_CAPI_EXPORT extern void TFE_CallDLManagedTensorDeleter(void* dlm_ptr);
}  // namespace tensorflow

        }

        VersionRange range = artifact.getVersionRange();
        if (range != null) {
            for (Restriction restriction : range.getRestrictions()) {
                if (isSnapshot(restriction.getLowerBound()) || isSnapshot(restriction.getUpperBound())) {
                    return RELEASE_OR_SNAPSHOT;
                }
            }
        }

        return RELEASE;
    }