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;

            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;
        }

        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.

        }

        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;
    }

		}
		p.errorf("wrong number of arguments to %s instruction", op)
		return
	case 4:
		if p.arch.Family == sys.S390X || p.arch.Family == sys.PPC64 {
			// 4-operand compare-and-branch.
			prog.From = a[0]
			prog.Reg = p.getRegister(prog, op, &a[1])
			prog.AddRestSource(a[2])
			target = &a[3]
			break
		}
		p.errorf("wrong number of arguments to %s instruction", op)
		return
	default:

const (
	RFP = -(iota + 1)
	RSB
	RSP
	RPC
)

// Arch wraps the link architecture object with more architecture-specific information.
type Arch struct {
	*obj.LinkArch
	// Map of instruction names to enumeration.
	Instructions map[string]obj.As
	// Map of register names to enumeration.
	Register map[string]int16
	// Table of register prefix names. These are things like R for R(0) and SPR for SPR(268).

// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

// This file encapsulates some of the odd characteristics of the ARM
// instruction set, to minimize its interaction with the core of the
// assembler.

package arch

import (
	"strings"

	"cmd/internal/obj"
	"cmd/internal/obj/arm"
)

var armLS = map[string]uint8{