-   Check if your changes are consistent with the
    [guidelines](#general-guidelines-and-philosophy-for-contribution).
-   Changes are consistent with the [Coding Style](#c-coding-style).
-   Run the [unit tests](#running-unit-tests).

## How to become a contributor and submit your own code

tf-keras-nightly==2.16.0.dev2024021410 \
    --hash=sha256:05a4c19c6a795ec9ed6f4dac69d443601b9206b910524d8b19bad6e362d49f47 \
    --hash=sha256:83d5a1dd3979b42164a2fac4bdd2ed5981ef0c2a0514ca64e4bc67e369caef5c
    # via tb-nightly
typing-extensions==4.8.0 \
    --hash=sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0 \
    --hash=sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef
    # via -r requirements.in

  const char* op_name_ = nullptr;
  // TODO(srbs): Use this.
  string device_name_;
};

// GraphContext wraps a TF_Graph modeling a single function and manages the
// "execution" of operation, i.e. adding them to the function.
class GraphContext : public TracingContext {
 public:
  explicit GraphContext(const char* name)
      : TracingContext(kGraph),
        graph_(new TF_Graph(), TF_DeleteGraph),

tf-keras-nightly==2.16.0.dev2024021410 \
    --hash=sha256:05a4c19c6a795ec9ed6f4dac69d443601b9206b910524d8b19bad6e362d49f47 \
    --hash=sha256:83d5a1dd3979b42164a2fac4bdd2ed5981ef0c2a0514ca64e4bc67e369caef5c
    # via tb-nightly
typing-extensions==4.8.0 \
    --hash=sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0 \
    --hash=sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef
    # via -r requirements.in

      if (!status->status.ok()) return;
      // TODO(nareshmodi): TFE_OpSetAttrFunction and TFE_OpSetAttrFunctionList
      // require TFE_Op* and just convert it internally a NameAttrValue, so
      // consider adding an overload to the C API to make this case easier.
      TFE_OpSetAttrFunction(op, attr_name, func_op);
      TFE_DeleteOp(func_op);
    } break;
    case tensorflow::AttrValue::kList: {
      // String

    return TF_SetStatus(status, TF_ALREADY_EXISTS, path);
  }

  auto metadata = gcs_file->gcs_client.InsertObject(
      bucket, object, "",
      // Adding this parameter means HTTP_CODE_PRECONDITION_FAILED
      // will be returned if the object already exists, so avoid reuploading.
      gcs::IfGenerationMatch(0), gcs::Fields(""));

  const char remote_device_name[] =
      "/job:localhost/replica:0/task:1/device:CPU:0";
  CheckRemoteMatMulExecutesOK(ctx, remote_device_name, local_device_name);

  // Adding a non-existent remote worker to cluster def. This should cause the
  // UpdateServerDef call to fail.
  tensorflow::ClusterDef* cluster_def = server_def.mutable_cluster();

// Operations the tape needs to perform on tensors to do backpropagation. Named
// "vspace" because a subset of these are related to a vector space, such as
// adding gradients, getting zeroes, etc. Currently cannot be implemented
// without using tensorflow python code, hence left unspecified here.
//
// Gradient is the type returned by gradient functions. In Python TF it's either

    // Make a copy so that we can mutate it.
    FunctionDef fdef_to_load = fdef;
    if (mutate_proto_func) {
      (*mutate_proto_func)(&fdef_to_load);
    }
    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(),

The links and the invocations' status can then be printed out, or output in the
form of JUnit-based XML.
"""
import argparse
import datetime
import os
import re
from typing import Dict, Union
import xml.etree.ElementTree as ElemTree


ResultDictType = Dict[str, Dict[str, Union[str, int]]]

RESULT_STORE_LINK_RE = re.compile(
    r'^INFO: Streaming build results to: (https://[\w./\-]+)')