target: devel
          build-args: |
            PYTHON_VERSION=${{ matrix.python-version }}
            CACHEBUSTER=${{ steps.date.outputs.DATE }}
          tags: |
            gcr.io/tensorflow-sigs/build:${{ github.event.number }}-${{ matrix.python-version }}
          cache-from: |
            type=registry,ref=tensorflow/build:latest-${{ matrix.python-version }}

-   Continuous jobs (Run on every GitHub commit)
    -   Uses `pycpp.sh`
-   Presubmit jobs (Run on every GitHub PR)
    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

permissions:
  contents: read

jobs:
  build:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    runs-on: [self-hosted, linux, ARM64]
    strategy:
      fail-fast: false
      matrix:
        pyver: ['3.9', '3.10', '3.11', '3.12']
    steps:
      - name: Stop old running containers (if any)
        shell: bash
        run: |
          running_containers=$(docker ps -q) && \

        "//tensorflow/core:framework",
        "//tensorflow/core:framework_internal_impl",
        "//tensorflow/core/lib/gtl:cleanup",
        "//tensorflow/core/platform:errors",
        "//tensorflow/core/platform:mutex",
        "//tensorflow/core/platform:refcount",
    ] + if_not_mobile([
        "//tensorflow/core/kernels:tensor_list",
        "//tensorflow/core/kernels:tensor_list_util",

                      /*inputs=*/{x.get()},
                      /*outputs=*/{},
                      /*use_function=*/UseFunction());
  ASSERT_EQ(errors::OK, s.code()) << s.message();
}

// Checks that inputs[0] is a matrix with shape 2x4.
Status TestTensorShape2x4(AbstractContext* ctx,
                          absl::Span<AbstractTensorHandle* const> inputs,
                          absl::Span<AbstractTensorHandle*> outputs) {

    std::list<Key>::iterator lra_iterator;
    /// The timestamp (seconds since epoch) at which the block was cached.
    uint64_t timestamp;
    /// Mutex to guard state variable
    absl::Mutex mu;
    /// The state of the block.
    FetchState state ABSL_GUARDED_BY(mu) = FetchState::CREATED;
    /// Wait on cond_var if state is FETCHING.
    absl::CondVar cond_var;
  };

  const std::string path;
  const bool is_cache_enable;
  const uint64_t buffer_size;
  ReadFn read_fn;
  absl::Mutex buffer_mutex;
  uint64_t buffer_start ABSL_GUARDED_BY(buffer_mutex);
  bool buffer_end_is_past_eof ABSL_GUARDED_BY(buffer_mutex);
  std::string buffer ABSL_GUARDED_BY(buffer_mutex);

  GCSFile(std::string path, bool is_cache_enable, uint64_t buffer_size,
          ReadFn read_fn)
      : path(path),

    TF_RETURN_IF_ERROR(tensorflow::TensorShapeUtils::MakeShape(dims, shape));

    return absl::OkStatus();
  }

  tensorflow::FullTypeDef FullType() const override {
    const FullTypeDef* ft;
    mutex_lock l(graph_->mu);
    graph_->graph.NodeType(output_.oper->node.name(), &ft);
    if (ft == nullptr) {
      return FullTypeDef();
    } else {
      return *ft;
    }
  }

  TF_Output output_;

    *   Update sklearn imports for deprecated packages.
    *   Deprecate `Variable.count_up_to` and `tf.count_up_to` in favor of
        `Dataset.range`.
    *   Export `confusion_matrix` op as `tf.math.confusion_matrix` instead of
        `tf.train.confusion_matrix`.
    *   Add `tf.dtypes.` endpoint for every constant in dtypes.py. Moving
        endpoints in versions.py to corresponding endpoints in `tf.sysconfig.`

#include "tensorflow/core/lib/monitoring/gauge.h"
#include "tensorflow/core/lib/monitoring/sampler.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/strcat.h"
#include "tsl/framework/cancellation.h"

using tensorflow::string;

void TFE_OpReset(TFE_Op* op_to_reset, const char* op_or_function_name,