TFE_TensorHandle* identity_ret[] = {nullptr, nullptr};
  num_retvals = 2;
  TFE_Execute(cloned_identity, identity_ret, &num_retvals, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_DeleteTensorHandle(input1);
  TFE_DeleteTensorHandle(input2);
  TFE_DeleteTensorHandle(identity_ret[0]);
  TFE_DeleteTensorHandle(identity_ret[1]);

  TFE_DeleteOp(cloned_identity);

    TFE_DeleteExecutor(executor);
    TFE_DeleteContext(ctx);
  }
  TF_DeleteStatus(status);
}

TEST(CAPI, Function_ident_CPU) {
  // First create a simple identity function.
  TF_Graph* function_graph = TF_NewGraph();
  TF_OperationDescription* arg_descr =
      TF_NewOperation(function_graph, "Placeholder", "arg");
  TF_SetAttrType(arg_descr, "dtype", TF_INT32);

contributors and maintainers pledge to make participation in our project and our
community a harassment-free experience for everyone, regardless of age, body
size, disability, ethnicity, gender identity and expression, level of
experience, nationality, personal appearance, race, religion, or sexual identity
and orientation.

## Our Standards

Examples of behavior that contributes to creating a positive environment include:

*   Using welcoming and inclusive language.

the local wheel file specified in the `requirements_lock_<python_version>.txt`.

Packages are imported one by one in alphabetical order during runtime.

The test doesn't identify package's order-dependent issues; for instance,
importing "tf.foo" followed by "tf.bar" won't reveal that "tf.bar" depends on
"tf.foo" being imported first.

you want to debug a kernel, you can compile specific files with `-g` using the
`--per_file_copt` bazel option. For example, if you want to debug the Identity
op, which are in files starting with `identity_op`, you can run

```bash
bazel build --config=dbg --per_file_copt=+tensorflow/core/kernels/identity_op.*@-g //tensorflow/tools/pip_package:build_pip_package
```

  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> reused_op(
      TFE_NewOp(context.get(), "Identity", status.get()), TFE_DeleteOp);
  TFE_OpReset(reused_op.get(), "Identity", custom_device_name, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  ASSERT_EQ(tensorflow::string(TFE_OpGetDevice(reused_op.get(), status.get())),

         "        value {"
         "          type: DT_FLOAT"
         "        }"
         "      }"
         "    }"
         "    node_def {"
         "      name: 'identity'"
         "      op: 'Identity'"
         "      input: 'add:z:0'"
         "      device: '/job:localhost/task:0/device:CPU:0'"
         "      attr {"
         "        key: 'T'"
         "        value {"

Network isolation between different models is also important not only to prevent
unauthorized access to data or models, but also to prevent malicious users or
tenants sending graphs to execute under another tenant’s identity.

The isolation mechanisms are the responsibility of the users to design and
implement, and therefore security issues deriving from their absence are not
considered a vulnerability in TensorFlow.

TFE_Op* AddOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b);

// Return a matmul op multiplying `a` by `b`.
TFE_Op* MatMulOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b);

// Return an identity op.
TFE_Op* IdentityOp(TFE_Context* ctx, TFE_TensorHandle* a);

// Return a shape op fetching the shape of `a`.
TFE_Op* ShapeOp(TFE_Context* ctx, TFE_TensorHandle* a);

                       AbstractTensorHandle** output, const char* name) {
  auto dtype = input->DataType();
  if (DataTypeIsFloating(BaseType(dtype)) ||
      DataTypeIsInteger(BaseType(dtype))) {
    return tensorflow::ops::Identity(ctx, input, output, name);
  } else if (!DataTypeIsComplex(BaseType(dtype)) &&
             BaseType(dtype) != DT_VARIANT) {
    return errors::InvalidArgument(
        "Expected numeric or variant tensor, got dtype ", dtype);