updating requirements for multiple minor versions of Python.

It takes in a file with a set of dependencies, and produces a more detailed
requirements file for each version, with hashes specified for each
dependency required, as well as their sub-dependencies.

### How to update/add requirements

By default, the name of the base requirements file is `requirements.in`, but it

    `TFCI` env param pointing to a list of `env` files.
2.  `utilities/setup.sh`, initialized by all top-level scripts, reads and sets
    values from those `TFCI` paths.
    -   `set -a` / `set -o allexport` exports the variables from `env` files so
        all scripts can use them.
    -   `utilities/setup_docker.sh` creates a container called `tf` with all

// TFE_NewTensorHandleFromDeviceMemory.
void DeallocatorWrapperFunc(void* data, size_t len, void* dlmt_vptr) {
  TFE_CallDLManagedTensorDeleter(dlmt_vptr);
}

// Checks whether the stride array matches the layout of compact, row-majored
// data.
bool IsValidStrideCompactRowMajorData(int64_t* shape_arr, int64_t* stride_arr,
                                      int ndim) {
  bool valid = true;

#include "tensorflow/c/eager/c_api.h"

#ifdef __cplusplus
extern "C" {
#endif

// Resets `op_to_reset` with `op_or_function_name` and `raw_device_name`. This
// is for performance optimization by reusing an exiting unused op rather than
// creating a new op every time. If `raw_device_name` is `NULL` or empty, it
// does not set the device name. If it's not `NULL`, then it attempts to parse

    GRU(recurrent_activation='hard_sigmoid', reset_after=False) to fallback to
    1.x behavior.

*   `CUDNN_INSTALL_PATH`, `TENSORRT_INSTALL_PATH`, `NCCL_INSTALL_PATH`,
    `NCCL_HDR_PATH` are deprecated. Use `TF_CUDA_PATHS` instead which supports a
    comma-separated list of base paths that are searched to find CUDA libraries
    and headers.

Refer to our

# ==============================================================================

name: Build SIG Build containers as presubmits

on:
  pull_request:
    types: [labeled, opened, synchronize, reopened]
    paths:
      - '.github/workflows/sigbuild-docker-presubmit.yml'
      - 'tensorflow/tools/tf_sig_build_dockerfiles/**'
      - '!tensorflow/tools/tf_sig_build_dockerfiles/README.md'

permissions:
  contents: read

jobs:

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
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.0.0.dev
tb-nightly ~= 2.17.0.a

potentially exploit a vulnerability in the TensorFlow code that handles videos,
which could allow them to execute arbitrary code on the system running
TensorFlow.

It is important to keep TensorFlow up to date with the latest security patches
and follow the sandboxing guideline above to protect against these types of
vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.

                                      std::unique_ptr<CustomDevice> device) = 0;

  // Return FunctionLibraryDefinition. Transformations need to use it to use it
  // to invoke MLIR compiler passes.
  virtual FunctionLibraryDefinition* FuncLibDef() = 0;

  // Resets the global rendezvous used for functions.
  virtual void ResetGlobalRendezvousForFunction() = 0;

              path, stat.generation_number)) {
        TF_VLog(
            1,
            "File signature has been changed. Refreshing the cache. Path: %s",
            path.c_str());
      }
      read = gcs_file->file_block_cache->Read(path, offset, n, buffer, status);
    } else {
      read = LoadBufferFromGCS(path, offset, n, buffer, gcs_file, status);
    }