#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

    issue_number: context.issue.number,
    owner: context.repo.owner,
    repo: context.repo.repo,
    labels: labels
  });
  if (resp_label.status >= 400) {
    console.log(resp_label);
    throw "Error adding labels to PR";
  }
  if (assignees.length > 0) {
    const resp_assign = await github.rest.issues.addAssignees({
      issue_number: context.issue.number,
      owner: context.repo.owner,

  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(),

    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

   
     * If you have above configuration and using _**Windows**_ platform -
       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable.
       * Refer [windows setup guide](https://www.tensorflow.org/install/gpu#windows_setup).
     * If you have above configuration and using _**Ubuntu/Linux**_ platform -
       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.

numpy ~= 1.23.5 ; python_version <= "3.11"
numpy ~= 1.26.0 ; python_version >= "3.12"
wheel ~= 0.41.2
h5py >= 3.10.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0

# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version