HasAMXTile          bool // Advanced Matrix Extension Tile instructions
	HasAMXInt8          bool // Advanced Matrix Extension Int8 instructions
	HasAMXBF16          bool // Advanced Matrix Extension BFloat16 instructions
	HasBMI1             bool // Bit manipulation instruction set 1
	HasBMI2             bool // Bit manipulation instruction set 2
	HasCX16             bool // Compare and exchange 16 Bytes

	R_ARM64_GOTPCREL

	// R_ARM64_GOT resolves a GOT-relative instruction sequence, usually an adrp
	// followed by another ld instruction.
	R_ARM64_GOT

	// R_ARM64_PCREL resolves a PC-relative addresses instruction sequence, usually an
	// adrp followed by another add instruction.
	R_ARM64_PCREL

	// R_ARM64_PCREL_LDST8 resolves a PC-relative addresses instruction sequence, usually an
	// adrp followed by a LD8 or ST8 instruction.

type encoding struct {
	encode   func(*instruction) uint32     // encode returns the machine code for an instruction
	validate func(*obj.Link, *instruction) // validate validates an instruction
	length   int                           // length of encoded instruction; 0 for pseudo-ops, 4 otherwise
}

var (
	// Encodings have the following naming convention:
	//
	//  1. the instruction encoding (R/I/S/B/U/J), in lowercase

const (
	// USES_REG_TMP indicates that a machine instruction generated from the
	// corresponding *obj.Prog uses the temporary register.
	USES_REG_TMP = 1 << iota

	// NEED_JAL_RELOC is set on JAL instructions to indicate that a
	// R_RISCV_JAL relocation is needed.
	NEED_JAL_RELOC

	// NEED_CALL_RELOC is set on an AUIPC instruction to indicate that it
	// is the first instruction in an AUIPC + JAL pair that needs a

	Len      int      // length of encoded instruction in bytes
	PCRel    int      // length of PC-relative address in instruction encoding
	PCRelOff int      // index of start of PC-relative address in instruction encoding
}

// Prefixes is an array of prefixes associated with a single instruction.
// The prefixes are listed in the same order as found in the instruction:

        is Instruction.CollectProjectScriptDependencies -> emitCollectProjectScriptDependencies(instruction.script)
        is Instruction.ApplyBasePlugins -> emitApplyBasePluginsTo()
        is Instruction.ApplyDefaultPluginRequests -> emitApplyEmptyPluginRequestsTo()
        is Instruction.ApplyPluginRequests -> emitApplyPluginRequests(instruction.requests, instruction.source)
        is Instruction.ApplyPluginRequestsOf -> {

Instead, the compiler operates on a kind of semi-abstract instruction set,
and instruction selection occurs partly after code generation.
The assembler works on the semi-abstract form, so
when you see an instruction like <code>MOV</code>
what the toolchain actually generates for that operation might
not be a move instruction at all, perhaps a clear or load.
Or it might correspond exactly to the machine instruction with that name.

//go:build gc

package main

// useVFPv1 tries to execute one VFPv1 instruction on ARM.
// It will crash the current process if VFPv1 is missing.
func useVFPv1()

// useVFPv3 tries to execute one VFPv3 instruction on ARM.
// It will crash the current process if VFPv3 is missing.
func useVFPv3()

// useARMv6K tries to run ARMv6K instructions on ARM.
// It will crash the current process if it doesn't implement

//go:build gc

#include "textflag.h"

// try to run "vmov.f64 d0, d0" instruction
TEXT ·useVFPv1(SB),NOSPLIT,$0
	WORD $0xeeb00b40	// vmov.f64 d0, d0
	RET

// try to run VFPv3-only "vmov.f64 d0, #112" instruction
TEXT ·useVFPv3(SB),NOSPLIT,$0
	WORD $0xeeb70b00	// vmov.f64 d0, #112
	RET

// try to run ARMv6K (or above) "ldrexd" instruction
TEXT ·useARMv6K(SB),NOSPLIT,$32
	MOVW R13, R2
	BIC  $15, R13

   
   
   *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.