TF_DataType dtype) {
  DCHECK(index >= 0 && index < shape_list->num_items);
  TF_ShapeAndType& shape_and_type = shape_list->items[index];
  shape_and_type.dtype = dtype;
}

void TF_DeleteShapeAndTypeList(TF_ShapeAndTypeList* shape_list) {
  if (shape_list == nullptr) return;
  for (size_t i = 0; i < shape_list->num_items; ++i) {

  // Consumes references to the tensors in the gradient_tensors list and returns
  // a tensor with the result.
  virtual Gradient* AggregateGradients(
      gtl::ArraySlice<Gradient*> gradient_tensors) const = 0;

  // Calls the passed-in backward function.
  //
  // `unneeded_gradients` contains sorted list of input indices for which a
  // gradient is not required.
  virtual Status CallBackwardFunction(

NCCL_LIB_PATHS = [
    'lib64/', 'lib/powerpc64le-linux-gnu/', 'lib/x86_64-linux-gnu/', ''
]

# List of files to configure when building Bazel on Apple platforms.
APPLE_BAZEL_FILES = [
    'tensorflow/lite/ios/BUILD', 'tensorflow/lite/objc/BUILD',
    'tensorflow/lite/swift/BUILD',
    'tensorflow/lite/tools/benchmark/experimental/ios/BUILD'
]

# List of files to move when building for iOS.
IOS_FILES = [

test:release_linux_base --build_tests_only --keep_going --test_output=errors --verbose_failures=true
test:release_linux_base --local_test_jobs=HOST_CPUS
test:release_linux_base --test_env=LD_LIBRARY_PATH
# Give only the list of failed tests at the end of the log
test:release_linux_base --test_summary=short

# Use the Clang toolchain to compile
build:release_cpu_linux --config=release_linux_base

# (error on undefined variables). This ensures that (a) every TFCI variable
# has an explicit default value, and (b) no script can accidentally use a
# variable that doesn't exist. Please keep this list in alphabetical order.
#
# Find usage in scripts with e.g.:
#   cd ci/official
#   ls *.sh utilities/*.sh | xargs grep -H --color=always TFCI_ARG_HERE
# You may also get an overview, e.g.:
#   cd ci/official

      if (std::find(indices.begin(), indices.end(), idx) != indices.end()) {
        return InvalidArgument("TF_Output ", node->name(), ":", idx,
                               " appears more than once in the input list");
      }
      indices.push_back(idx);
    }
  }
  return absl::OkStatus();
}

// Converts `noutputs` and `outputs` into `outputs_tensors` and does various
// checks while doing so.

keras-nightly==3.0.4.dev2024021403 \
    --hash=sha256:24ce69d29d582771685bf4235f59663723405b5a5b16f3eaff2657e52e74663a \
    --hash=sha256:9f416e66b820ef833779d219d255b346b8b90a72fdbd0b2f1e90a43ad142a03d
    # via -r requirements.in
lit==17.0.6 \
    --hash=sha256:dfa9af9b55fc4509a56be7bf2346f079d7f4a242d583b9f2e0b078fd0abae31b
    # via -r requirements.in
markdown==3.5.2 \
    --hash=sha256:d43323865d89fc0cb9b20c75fc8ad313af307cc087e84b657d9eec768eddeadd \

keras-nightly==3.0.4.dev2024021403 \
    --hash=sha256:24ce69d29d582771685bf4235f59663723405b5a5b16f3eaff2657e52e74663a \
    --hash=sha256:9f416e66b820ef833779d219d255b346b8b90a72fdbd0b2f1e90a43ad142a03d
    # via -r requirements.in
lit==17.0.6 \
    --hash=sha256:dfa9af9b55fc4509a56be7bf2346f079d7f4a242d583b9f2e0b078fd0abae31b
    # via -r requirements.in
markdown==3.5.2 \
    --hash=sha256:d43323865d89fc0cb9b20c75fc8ad313af307cc087e84b657d9eec768eddeadd \