snprintf(file_name, sizeof(file_name), "test-%d-%ld.txt", getpid(), t);

  size_t length = 2 + strlen(path) + strlen(file_name);
  char* full_path = malloc(length);
  snprintf(full_path, length, "%s/%s", path, file_name);

  TF_WritableFileHandle* h;
  TF_Status* status = TF_NewStatus();
  TF_NewWritableFile(full_path, &h, status);
  if (TF_GetCode(status) != TF_OK) {
    fprintf(stderr, "TF_NewWritableFile failed: %s\n", TF_Message(status));

}

void TF_SetFullType(TF_Graph* graph, TF_Operation* op,
                    const TF_Buffer* full_type_proto) {
  using tensorflow::RecordMutation;
  mutex_lock l(graph->mu);
  FullTypeDef full_type;
  full_type.ParseFromArray(full_type_proto->data, full_type_proto->length);
  *op->node.mutable_def()->mutable_experimental_type() = full_type;
  RecordMutation(graph, *op, "setting fulltype");
}

    *   `tf.data.Dataset.zip` now supports Python-style zipping, i.e. `Dataset.zip(a, b, c)`.
    * `tf.data.Dataset.shuffle` now supports `tf.data.UNKNOWN_CARDINALITY` When doing a "full shuffle" using  `dataset = dataset.shuffle(dataset.cardinality())`. But remember, a "full shuffle" will load the full dataset into memory so that it can be shuffled, so make sure to only use this with small datasets or datasets of small objects (like filenames).

*   `tf.math`

    (by default) transferred to the TensorFlow team. This means that the benefit
    of the contribution must be compared against the cost of maintaining the
    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in
    [tensorflow/addons](https://github.com/tensorflow/addons) to get some

add them to //tensorflow/tools/pip_package/BUILD. Otherwise, add the no_pip tag
to the test, or change code_check_full.bats in the SIG Build repository. That's
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/tf_sig_build_dockerfiles/devel.usertools/code_check_full.bats
Here are the affected tests:
EOF
    while read dep; do
      echo "For dependency $dep:"

config_setting(
    name = "lite_protos_legacy",
    define_values = {"TENSORFLOW_PROTOS": "lite"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "full_protos",
    define_values = {"TENSORFLOW_PROTOS": "full"},
    visibility = ["//visibility:public"],
)

selects.config_setting_group(
    name = "lite_protos",
    match_any = [":lite_protos_legacy"],

#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_helper.h"
#include "xla/tsl/c/tsl_status_internal.h"
#include "tensorflow/core/framework/full_type.pb.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"

  TF_Buffer* ret = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(config, ret));
  return ret;
}

TF_Buffer* TF_CreateRunOptions(unsigned char enable_full_trace) {
  tensorflow::RunOptions options;
  if (enable_full_trace) {
    options.set_trace_level(tensorflow::RunOptions::FULL_TRACE);
  } else {
    options.set_trace_level(tensorflow::RunOptions::NO_TRACE);
  }
  TF_Buffer* ret = TF_NewBuffer();

      looping.
  """
  default = environ_cp.get(var_name) or var_default
  full_query = '%s [Default is %s]: ' % (
      ask_for_var,
      default,
  )

  for _ in range(n_ask_attempts):
    val = get_from_env_or_user_or_default(environ_cp, var_name, full_query,
                                          default)
    if check_success(val):
      break

  // Deletes the input tensor.
  virtual void DeleteGradient(Gradient* gradient) const = 0;
};

// Traces the execution of operations, doing eager garbage collection, and
// exporting a full trace so other code can do backpropagation. Not thread-safe.
template <typename Gradient, typename BackwardFunction, typename TapeTensor>
class GradientTape {
 public: