#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/graph/validate.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/platform/coding.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/mem.h"
#include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/status.h"

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

# REQUIREMENTS_FILE=jax.requirements.txt


setuptools
wheel
cloudpickle
colorama>=0.4.4
matplotlib
pillow>=9.1.0
rich
absl-py
portpicker
six
opt-einsum
auditwheel
typing_extensions
importlib_metadata>=4.6
numpy==1.26.0;python_version=="3.12"
numpy==1.23.4;python_version=="3.11"
numpy==1.22.4;python_version<"3.11"
scipy==1.11.2;python_version=="3.12"
scipy==1.9.2;python_version=="3.11"

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./\-]+)')

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

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

// 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(),

    question: optional string for how to ask for user input.
    yes_reply: optional string for reply when feature is enabled.
    no_reply: optional string for reply when feature is disabled.
    bazel_config_name: adding config to .bazelrc instead of action_env.
  """
  var = int(
      get_var(environ_cp, var_name, query_item, enabled_by_default, question,
              yes_reply, no_reply))

  if not bazel_config_name:

-   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