namespace tracing {

Status MaybeSetOpName(AbstractOperation* op, const char* op_name) {
  if (isa<TracingOperation>(op)) {
    TF_RETURN_IF_ERROR(dyn_cast<TracingOperation>(op)->SetOpName(op_name));
  }
  if (isa<gradients::TapeOperation>(op)) {
    TF_RETURN_IF_ERROR(MaybeSetOpName(
        dyn_cast<gradients::TapeOperation>(op)->GetBackingOperation(),
        op_name));
  }
  return absl::OkStatus();
}
}  // namespace tracing

      pull-requests: write
    if: |
      github.event.pull_request.draft == false &&
      github.event.sender.type == 'User'
    steps:
      - name: Checkout repo
        uses: actions/checkout@755da8c3cf115ac066823e79a1e1788f8940201b # v3.2.0
      - name: Trusted-Partners-PR
        uses: actions/github-script@d556feaca394842dc55e4734bf3bb9f685482fa0 # v6.3.3
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}

  std::string TranslateName(const std::string& name) const override;
  void FlushCaches(TransactionToken* token) override;
  Status SetOption(const std::string& name,
                   const std::vector<string>& values) override;
  Status SetOption(const std::string& name,
                   const std::vector<int64_t>& values) override;
  Status SetOption(const std::string& name,

---
name: TensorFlow Lite Converter Issue
about: Use this template for reporting issues during model conversion to TFLite
labels: 'TFLiteConverter'

---

### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installation (pip package or built from source):
- TensorFlow library (version, if pip package or github SHA, if built from source):

### 2. Code

int64_t TFE_MonitoringCounterReader::Read(const LabelType&... labels) {
  return counter->Read(labels...);
}

TFE_MonitoringCounterReader* TFE_MonitoringNewCounterReader(const char* name) {
  auto* result = new TFE_MonitoringCounterReader(name);

  return result;
}

int64_t TFE_MonitoringReadCounter0(TFE_MonitoringCounterReader* cell_reader) {
  int64_t result = cell_reader->Read();

  return result;
}

REQUIREMENTS_LOCK_FILE="requirements_lock_${PYTHON_VERSION}.txt"
touch "$REQUIREMENTS_LOCK_FILE"

### Update the requirements_lock file
bazel run --experimental_convenience_symlinks=ignore --repo_env=REQUIREMENTS_FILE_NAME=requirements_wheel_test.in //:requirements_${PYTHON_VERSION}.update

# Move the updated file to the appropriate directory
mv "$REQUIREMENTS_LOCK_FILE" ../wheel_test/

struct ForwardOperation {
 public:
  string op_name;
  std::vector<AbstractTensorHandle*> inputs;
  std::vector<AbstractTensorHandle*> outputs;
  std::vector<int64_t> skip_input_indices;
  AttrBuilder attrs;
};

using GradientFunctionFactory =
    std::function<GradientFunction*(const ForwardOperation& op)>;

// Map from op name to a `GradientFunctionFactory`.
class GradientRegistry {
 public:

# This is the official list of TensorFlow authors for copyright purposes.
# This file is distinct from the CONTRIBUTORS files.
# See the latter for an explanation.

# Names should be added to this file as:
# Name or Organization <email address>
# The email address is not required for organizations.

Google Inc.
Yuan Tang <******@****.***>

    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }
    ],
    "access_right": "open",

      comment syntax for the file format. We also recommend that a
      file or class name and description of purpose be included on the
      same "printed page" as the copyright notice for easier
      identification within third-party archives.

   Copyright [yyyy] [name of copyright owner]

   Licensed under the Apache License, Version 2.0 (the "License");