# Bazelisk manually. We enable this for macOS x86 builds as those VMs do not
# have Bazelisk pre-installed. "TFCI_MACOS_INSTALL_BAZELISK_URL" contains the
# link to the Bazelisk binary which needs to be downloaded.
if [[ "${TFCI_MACOS_INSTALL_BAZELISK_ENABLE}" == 1 ]]; then
  sudo wget --no-verbose -O "/usr/local/bin/bazel" "${TFCI_MACOS_INSTALL_BAZELISK_URL}"
  chmod +x "/usr/local/bin/bazel"
fi

           TempFile(temp_file_name, std::ios::binary | std::ios::trunc), false,
           static_cast<int64_t>(metadata->size())});
    } else if (TF_GetCode(status) == TF_NOT_FOUND) {
      file->plugin_file = new tf_writable_file::GCSFile(
          {std::move(bucket), std::move(object), &gcs_file->gcs_client,
           TempFile(temp_file_name, std::ios::binary | std::ios::trunc), true,
           0});
    } else {

* <IF A CHANGE CLOSES A GITHUB ISSUE, IT SHOULD BE DOCUMENTED HERE>
* <NOTES SHOULD BE GROUPED PER AREA>

* GPU
    * Support for NVIDIA GPUs with compute capability 8.9 (e.g. L4 & L40) has
      been added to TF binary distributions (Python wheels).
* Replace `DebuggerOptions` of TensorFlow Quantizer, and migrate to
  `DebuggerConfig` of StableHLO Quantizer.

## Keras

    }
    if (lowercased_title.includes('tf')) {
      assignees.push('rohan100jain', 'bfontain');
    }
  }
  if (lowercased_title.includes('linaro') && domain.includes('linaro.org')) {
    if (lowercased_title.includes('arm_ci')) {
      assignees.push('nitins17', 'penpornk');
    }
  }
  if (lowercased_title.includes('tf-mot') && lowercased_title.includes('arm') &&

    }
    VLOG(1) << "Adding func to graph: " << fdef_to_load.DebugString();
    std::vector<char> binary_proto_buf(fdef_to_load.ByteSizeLong());
    fdef_to_load.SerializeToArray(binary_proto_buf.data(),
                                  binary_proto_buf.size());
    TF_Function* func = TF_FunctionImportFunctionDef(
        binary_proto_buf.data(), binary_proto_buf.size(), status);
    if (!status->status.ok()) return {};

        'can be specified as "x.y" or "compute_xy" to include both virtual and'
        ' binary GPU code, or as "sm_xy" to only include the binary '
        'code.\nPlease note that each additional compute capability '
        'significantly increases your build time and binary size, and that '
        'TensorFlow only supports compute capabilities >= 3.5 [Default is: '

# Debug config
build:dbg -c dbg
# Only include debug info for files under tensorflow/, excluding kernels, to
# reduce the size of the debug info in the binary. This is because if the debug
# sections in the ELF binary are too large, errors can occur. See
# https://github.com/tensorflow/tensorflow/issues/48919.
# Users can still include debug info for a specific kernel, e.g. with:

**Libtensorflow Windows GPU** | Status Temporarily Unavailable                                                                                                                                                   | [Nightly Binary](https://storage.googleapis.com...

# proto-message: ActionSpecs

# Python rules should not have more than one source file.
action_spec {
  action: CHECK_FILE_COUNT
  file_count_params {
    rule_selector {
      rule_kind_regex: "^.*py(type)?(_strict)?_(binary|library|test).*$"
      generator_function_regex: "^(?!boq_header)$"
    }
    max_source_count: 1
  }

about: Use this template for reporting Lite ops you are using or missing
labels: 'comp:lite'

---

**System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installed from (source or binary):
- TensorFlow version (or github SHA if from source):


**Provide the text output from tflite_convert**

```
# Copy and paste here
```

**Standalone code to reproduce the issue**