**2. Installing **TensorFlow** (TF) CPU prebuilt binaries**
   
   
   *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:
   

        if: contains(github.event.pull_request.labels.*.name, 'build and push to gcr.io for staging')
        with:
          repo-token: ${{ secrets.GITHUB_TOKEN }}
          message: |
            I pushed these containers:
            
            - `gcr.io/tensorflow-sigs/build:${{ github.event.number }}-python3.12`
            - `gcr.io/tensorflow-sigs/build:${{ github.event.number }}-python3.11`

-       | ((const unsigned char *) (const char *) (src))[idx + 2]) << 8	      \
-      | ((const unsigned char *) (const char *) (src))[idx + 3])
-# endif
 #else
 /* These are a few types we need for the optimizations if we cannot
    use unaligned memory accesses.  */
@@ -94,148 +72,11 @@ __STRING2_COPY_TYPE (8);
 
 /* Set N bytes of S to C.  */
 #if !defined _HAVE_STRING_ARCH_memset

on:
  workflow_dispatch:
    inputs:
      # We use this instead of the "run on branch" argument because GitHub looks
      # on that branch for a workflow.yml file, and we'd have to cherry-pick
      # this file into those branches.
      release_branch:
        description: 'Release branch name (e.g. r2.9)'
        required: true
        type: string
      git_commit:
        description: 'Git commit to cherry-pick'