/tensorflow/tools/git/gen
/pip_test
/_python_build
*.pyc
__pycache__
*.swp
.vscode/
cmake_build/
tensorflow/contrib/cmake/_build/
.idea/**
/build/
[Bb]uild/
/build_output/
/tensorflow/core/util/version_info.cc
/tensorflow/python/framework/fast_tensor_util.cpp
/tensorflow/lite/gen/**
/tensorflow/lite/tools/make/downloads/**
/tensorflow/lite/tools/make/gen/**
/api_init_files_list.txt
/estimator_api_init_files_list.txt
*.whl

  for (const auto* edge : oper->node.out_edges()) {
    if (edge->IsControlEdge() && !edge->dst()->IsSink()) {
      ++count;
    }
  }
  return count;
}

int TF_OperationGetControlOutputs(TF_Operation* oper,
                                  TF_Operation** control_outputs,
                                  int max_control_outputs) {
  int count = 0;
  for (const auto* edge : oper->node.out_edges()) {

    const TF_Graph* fn_body, const char* fn_name, int ninputs,
    const TF_Output* inputs, std::vector<OutputTensor>* input_tensors,
    std::unordered_map<const Node*, std::vector<int>>* input_nodes)
    TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  input_tensors->reserve(ninputs);
  for (int i = 0; i < ninputs; ++i) {
    Node* node = inputs[i].oper ? &inputs[i].oper->node : nullptr;
    int idx = inputs[i].index;

# JAX requirements, passed into container by defining the ARG 
# 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"

    *   Added 8-bit/16-bit/32-bit int/uint support for the built-in op `right_shift`
    *   Added reference implementation for 16-bit int unquantized `add`.
    *   Added reference implementation for 16-bit int and 32-bit unsigned int unquantized `mul`.
    *   `add_op` supports broadcasting up to 6 dimensions.
    *   Added 16-bit support for `top_k`.

*   `tf.function`

    return absl::OkStatus();
  }
  Status SetAttrShapeList(const char* attr_name, const int64_t** dims,
                          const int* num_dims, int num_values) override {
    std::vector<PartialTensorShape> shapes;
    shapes.reserve(num_values);
    for (int i = 0; i < num_values; ++i) {
      if (num_dims[i] < 0) {
        shapes.emplace_back();
      } else {
        shapes.emplace_back(ArraySlice<int64_t>(

# 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:

  tfrun python3 tensorflow/tools/ci_build/update_version.py --nightly
fi
source ci/official/utilities/get_versions.sh

# Note on gsutil commands:
# "gsutil cp" always "copies into". It cannot act on the contents of a directory
# and it does not seem possible to e.g. copy "gs://foo/bar" as anything other than
# "/path/bar". This script uses "gsutil rsync" instead, which acts on directory

)

config_setting(
    name = "with_xla_support",
    define_values = {"with_xla_support": "true"},
    visibility = ["//visibility:public"],
)

# By default, XLA GPU is compiled into tensorflow when building with
# --config=cuda even when `with_xla_support` is false. The config setting
# here allows us to override the behavior if needed.
config_setting(
    name = "no_xla_deps_in_cuda",

      # Get the linker version
      ld_version = run_shell([gcc_env, '-Wl,-version']).split()

      ld_version_int = convert_version_to_int(ld_version[3])
      if ld_version_int is None:
        ld_version_int = convert_version_to_int(ld_version[4])

      # Enable if 'ld' version >= 2.35
      if ld_version_int >= 2035000:
        write_to_bazelrc(
            'build --copt="-DEIGEN_ALTIVEC_ENABLE_MMA_DYNAMIC_DISPATCH=1"')