auto fcmp = block_map_.upper_bound(fmax);
    if (fcmp != block_map_.begin() && key < (--fcmp)->first) {
      return TF_SetStatus(status, TF_INTERNAL,
                          "Block cache contents are inconsistent.");
    }
  }

  Trim();

  return TF_SetStatus(status, TF_OK, "");
}

void RamFileBlockCache::MaybeFetch(const Key& key,

#include "tensorflow/core/platform/test.h"

using tensorflow::Status;
using tensorflow::string;
using tensorflow::TF_StatusPtr;

namespace tensorflow {
namespace {

// The tests are parameterized on:
// - a string representing the tracing implementation: "mlir" or "graphdef".
// - a boolean that when true enables TFRT as the execution engine.
class UnifiedCAPI

#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/file_system_helper.h"
#include "tsl/platform/errors.h"

// TODO(b/139060984): After all filesystems are converted, all calls to
// methods from `FileSystem` will have to be replaced to calls to private
// methods here, as part of making this class a singleton and the only way to
// register/use filesystems.

  EXPECT_TRUE(absl::StrContains(s, expected))
      << "'" << s << "' does not contain '" << expected << "'";
}

// Returns the GPU device name if there is one (with arbitrary tie breaking if
// there are more than one), or "" otherwise.
string GPUDeviceName(TF_Session* session) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TF_Status* s = status.get();

    if (TF_GetCode(status.get()) != TF_OK) {
      ADD_FAILURE() << tag << " -- " << TF_Message(status.get());
      continue;
    }
    TFE_DeleteTensorHandle(hdevice2);

    // Ensure that the contents are the same!
    TF_Tensor* tcopy = TFE_TensorHandleResolve(hcopy, status.get());
    TFE_DeleteTensorHandle(hcopy);
    if (TF_GetCode(status.get()) != TF_OK) {
      ADD_FAILURE() << tag;
      continue;
    }

    which it occurs.

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## Our Responsibilities

# the dependencies of that target which include a license notice file are then
# included when generating that license. Necessary because the license targets
# in TensorFlow are manually enumerated rather than generated automatically.
do_external_licenses_check(){
  BUILD_TARGET="$1"
  LICENSES_TARGET="$2"

  # grep patterns for targets which are allowed to be missing from the licenses

-   Ensure you have signed the
    [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
-   Check if your changes are consistent with the
    [guidelines](#general-guidelines-and-philosophy-for-contribution).
-   Changes are consistent with the [Coding Style](#c-coding-style).
-   Run the [unit tests](#running-unit-tests).

## How to become a contributor and submit your own code

TFCI=py312,linux_x86_cuda,public_cache,disk_cache ci/official/wheel.sh

# First, set your TFCI variable to choose the environment settings.
#   TFCI is a comma-separated list of filenames from the envs directory, which
#   are all settings for the scripts. TF's CI jobs are all made of a combination
#   of these env files.
#
#   If you've clicked on a test result from our CI (via a dashboard or GitHub link),

# Cuda Comment
cuda_comment: >
   From the template it looks like you are installing **TensorFlow** (TF) prebuilt binaries:
      * For TF-GPU - See point 1
      * For TF-CPU - See point 2
   -----------------------------------------------------------------------------------------------
   
   **1. Installing **TensorFlow-GPU** (TF) prebuilt binaries**
   
   
   Make sure you are using compatible TF and CUDA versions.