dtype.code = DLDataTypeCode::kDLBool;
      break;
    case TF_DataType::TF_HALF:
    case TF_DataType::TF_FLOAT:
    case TF_DataType::TF_DOUBLE:
      dtype.code = DLDataTypeCode::kDLFloat;
      break;
    case TF_DataType::TF_INT8:
    case TF_DataType::TF_INT16:
    case TF_DataType::TF_INT32:
    case TF_DataType::TF_INT64:
      dtype.code = DLDataTypeCode::kDLInt;
      break;
    case TF_DataType::TF_UINT8:

for an example.

See
[this commit](https://github.com/tensorflow/tensorflow/commit/5f7f05a80aac9b01325a78ec3fcff0dbedb1cc23)
as a rough example of the steps below.

All the files referenced below are located in the same directory as this README,
unless indicated otherwise.

1) Add the new version to the `VERSIONS` variable inside
   `tensorflow/tools/toolchains/python/python_repo.bzl`. \

index e966c03..22ab790 100644
--- a/locale/weightwc.h
+++ b/locale/weightwc.h
@@ -79,19 +79,19 @@ findidx (const wint_t **cpp, size_t len)
 	    if (cp[cnt] != usrc[cnt])
 	      break;
 
-	  if (cnt < nhere - 1)
+	  if (cnt < nhere - 1 || cnt == len)
 	    {
 	      cp += 2 * nhere;
 	      continue;
 	    }
 
-	  if (cp[nhere - 1] > usrc[nhere -1])

```
(You can paste links or attach files by dragging & dropping them below)
- Provide links to your updated versions of the above two colab notebooks.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```

#### Option B: Paste your code here or provide a link to a custom end-to-end colab

```
(You can paste links or attach files by dragging & dropping them below)

#      Ex. public_cache_push -- Use TF's public cache (read and write, Googlers only)
#      Ex. rbe        -- Use RBE for faster builds (Googlers only; see below)
#      Ex. no_docker  -- Disable docker on enabled platforms
#    See full examples below for more details on these. Some other modifiers are:
#      Ex. versions_upload -- for TF official release versions

  # In case the main script fails somehow.
  cat <<EOF
IMPORTANT: For bazel invocations that uploaded to ResultStore (e.g. RBE), you
can view more detailed results that are probably easier to read than this log.
Try the links below:
EOF
  # Find any "Streaming build results to" line, then print the last word in it,
  # and don't print duplicates
  awk '/Streaming build results to/ {print $NF}' "$TFCI_OUTPUT_DIR/script.log" | uniq
}

   
   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.

    // Now deleting the directory should work.
    TF_DeleteDir(dirpath.c_str(), s);
    ASSERT_TF_OK(s) << "TF_DeleteDir failed for " << dirpath << ": "
                    << TF_Message(s);

    TF_DeleteStatus(s);
    break;
  }

  ASSERT_TRUE(found) << "expected at least one temp dir";

  TF_StringStreamDone(tempdirs);
}

TEST(TestEnv, TestTimeFunctions) {
  ASSERT_GE(TF_NowSeconds(), 946684800);  // Midnight Jan 1, 2000

//   * `void* ext` is a free-form field that can be populated by
//     a plugin in `TP_*` structs or potential future extension points .
//
// Example usage:
//
//   /* Sample TensorFlow code below, exact implementation might differ. */
//   // Version checking uses `struct_size`. It should be set both by core
//   // and the plugin.
//   TP_OptimizerRegistrationParams params{

          ->NodesToPreserve();
  char* p = static_cast<char*>(storage);

  int index = 0;
  for (const std::string& s : nodes) {
    if (index >= num_values) break;
    values[index] = p;
    lengths[index] = s.size();
    if ((p + s.size()) > (static_cast<char*>(storage) + storage_size)) {
      tsl::Set_TF_Status_from_Status(
          status,