-   type: input id: tfversion attributes: label: Tensorflow Version description:
    placeholder: ex,. tf 2.8 validations: required: true

-   type: dropdown id: Code attributes: label: Custom Code description:
    options: - "Yes" - "No" validations: required: true

-   type: input id: OS attributes: label: OS Platform and Distribution
    description: placeholder: e.g., Linux Ubuntu 16.04 validations: required:

- Provide links to your updated versions of the above two colab notebooks.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```

#### Option B: Paste your code here or provide a link to a custom end-to-end colab

```
(You can paste links or attach files by dragging & dropping them below)
- Include code to invoke the TFLite Converter Python API and the errors.

        "//tensorflow/core:test_main",
        "@com_google_absl//absl/strings",
    ],
)

cc_library(
    name = "custom_device_testutil",
    testonly = True,
    srcs = ["custom_device_testutil.cc"],
    hdrs = ["custom_device_testutil.h"],
    visibility = ["//tensorflow:internal"],
    deps = [
        ":c_api",
        ":c_api_experimental",
        ":c_api_test_util",

# Build a shared library (DLL) by cc_binary from tf_custom_op_library_additional_deps_impl,
# it contains all object code from its dependencies.
# This target is only used for parsing the symbols to be exported in tensorflow.dll.
# Do NOT depend on it.
tf_native_cc_binary(
    name = "tf_custom_op_library_additional_deps.dll",
    linkshared = 1,
    linkstatic = 1,
    deps = tf_custom_op_library_additional_deps_impl(),
)

*   `tf.saved_model`:

    *   SavedModels can now save custom gradients. Use the option
        `tf.saved_model.SaveOption(experimental_custom_gradients=True)` to
        enable this feature. The documentation in
        [Advanced autodiff](https://www.tensorflow.org/guide/advanced_autodiff#custom_gradients)
        has been updated.

        ":not_differentiable",
        "//tensorflow/c/eager:abstract_context",
        "//tensorflow/c/eager:gradients_internal",
    ],
)

tf_cuda_cc_test(
    name = "custom_gradient_test",
    size = "small",
    srcs = [
        "custom_gradient_test.cc",
    ],
    args = ["--heap_check="],  # TODO(b/174752220): Remove
    tags = tf_cuda_tests_tags(),
    deps = [
        "//tensorflow/c:tf_status_helper",

computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers...

load("@local_tsl//tsl/platform:rules_cc.bzl", "cc_library")
load(
    "//tensorflow:tensorflow.bzl",
    "check_deps",
    "if_google",
    "if_not_mobile",
    "tf_cc_test",
    "tf_copts",
    "tf_cuda_library",
    "tf_custom_op_library",
    "tf_kernel_library",
)
load("//tensorflow:tensorflow.default.bzl", "filegroup", "tf_cuda_cc_test")
load(
    "//tensorflow/core/tpu:build_defs.bzl",
    "if_libtpu_tf_status",
)

package(

[Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)

Use `pylint` to check your Python changes. To install `pylint` and check a file
with `pylint` against TensorFlow's custom style definition:

```bash
pip install pylint
pylint --rcfile=tensorflow/tools/ci_build/pylintrc myfile.py
```

Note `pylint --rcfile=tensorflow/tools/ci_build/pylintrc` should run from the