private:
  friend class GraphContext;  // For access to op_.
  TF_Graph* g_;
  std::unique_ptr<TF_OperationDescription> op_;
  // Hold `op_type` and `op_name` till both are available since we need both
  // to build a graph operation.
  string op_type_;
  const char* op_name_ = nullptr;
  // TODO(srbs): Use this.
  string device_name_;
};

    return nullptr;
  }
  std::vector<int64_t> dimvec(num_dims);
  for (int i = 0; i < num_dims; ++i) {
    dimvec[i] = static_cast<int64_t>(dims[i]);
  }

  // TODO(apassos) do we need to wrap the deallocator here to make sure to sync
  // the device?
  TF_ManagedBuffer* buf =
      new TF_ManagedBuffer(data, len, deallocator, deallocator_arg,
                           /*owns_memory=*/false);

    visibility = ["//visibility:public"],
)

# DO NOT ADD ANY NEW EXCEPTIONS TO THIS LIST!
# Instead, please use public APIs or public build rules TF provides.
# If you need functionality that is not exposed, we will work with you to expand our public APIs.
# TODO(b/173549186): Move Google-internal TF code out of learning/brain
package_group(
    name = "internal",
    packages = [

    environ_cp['TF_NEED_CUDA'] = '0'
  else:
    environ_cp['TF_NEED_CUDA'] = str(
        int(get_var(environ_cp, 'TF_NEED_CUDA', 'CUDA', False)))
  if (environ_cp.get('TF_NEED_CUDA') == '1' and
      'TF_CUDA_CONFIG_REPO' not in environ_cp):

    set_action_env_var(
        environ_cp,
        'TF_NEED_TENSORRT',
        'TensorRT',
        False,
        bazel_config_name='tensorrt')

//
// Gradient is the type returned by gradient functions. In Python TF it's either
// Tensor or IndexedSlices or None, which here we map to nullptr. Gradients need
// to allow their size to be computed and they need to be passable to a backward
// function and deleted (as the backprop code creates lots of gradients the user
// is not interested in).
//

//
// In general, the `TF_InitPlugin` symbol doesn't need to be exposed in a header
// file, since the plugin registration will look for the symbol in the DSO file
// that provides the filesystem functionality. However, the POSIX filesystem
// needs to be statically registered in some tests and utilities for building
// the API files at the time of creating the pip package. Hence, we need to

build:tpu --copt=-DLIBTPU_ON_GCE
build:tpu --define=enable_mlir_bridge=true

build:tensorrt --repo_env TF_NEED_TENSORRT=1

build:rocm --crosstool_top=@local_config_rocm//crosstool:toolchain
build:rocm --define=using_rocm_hipcc=true
build:rocm --define=tensorflow_mkldnn_contraction_kernel=0
build:rocm --repo_env TF_NEED_ROCM=1

build:sycl --crosstool_top=@local_config_sycl//crosstool:toolchain
build:sycl --define=using_sycl=true

    [Environment]::SetEnvironmentVariable(\"PATH\", $old_path + \";C:\VS\VC\Tools\MSVC\14.33.31629\bin\Hostx64\x64;C:\VS\Common7\Tools;C:\VS\MSBuild\Current\Bin\", \"Machine\");

# Add signtool.exe to the PATH. Note this path may need to be edited if updates
# are made to the Windows 10 SDK.
RUN $old_path = [Environment]::GetEnvironmentVariable(\"PATH\", \"Machine\"); \

//tensorflow/core/tfrt
//tensorflow/python/kernel_tests/signal
//tensorflow/examples
//tensorflow/tools/android
//tensorflow/python/eager/benchmarks
EOF

  # grep patterns for files and targets which don't need to be in the pip
  # package, ever.
  cat > $BATS_TEST_TMPDIR/ignore_these_deps <<EOF
benchmark
_test$
_test.py$
_test_cpu$
_test_cpu.py$
_test_gpu$
_test_gpu.py$
_test_lib$

//     OK to not have the inputs properly set in `op`. See `input_tensors`
//     if you want shape inference to consider the input tensors of the
//     op for shape inference.
//   - The types need not be set in `input_shapes` as it is not used.
//   - The number of `input_tensors` should be the same as the number of items
//     in `input_shapes`.
//
// The results are returned in `output_shapes` and