---
name: TensorFlow Lite Op Request
about: Use this template for reporting Lite ops you are using or missing
labels: 'comp:lite'

---

**System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installed from (source or binary):
- TensorFlow version (or github SHA if from source):


**Provide the text output from tflite_convert**

```
# Copy and paste here
```

autogenerated from the registered Op and API definitions.

To regenerate them, run the script in this directory, `update_cpp_ops.sh`, with
no arguments. This script will overwrite the existing ops in-place at
***tensorflow/c/experimental/ops/\*_ops.{cc,h}***.

Run this `update_cpp_ops.sh` script when Op definitions change in the registry.

        "//tensorflow/core:core_cpu_internal",
        "//tensorflow/core:direct_session",
        "//tensorflow/core:framework",
        "//tensorflow/core:framework_internal",
        "//tensorflow/core:functional_ops_op_lib",
        "//tensorflow/core:lib",
        "//tensorflow/core:math_ops_op_lib",
        "//tensorflow/core:nn_ops_op_lib",
        "//tensorflow/core:no_op_op_lib",

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
```

        "//tensorflow/c/eager:c_api",
        "//tensorflow/c/eager:c_api_experimental",
        "//tensorflow/c/eager:tfe_cancellation_manager_internal",
        "//tensorflow/c/eager:tfe_op_internal",
        "//tensorflow/c/eager:tfe_tensorhandle_internal",
        "//tensorflow/core:framework",
        "//tensorflow/core:lib",
        "@com_google_absl//absl/types:optional",

)

cc_library(
    name = "tfe_op_attrs_internal",
    hdrs = ["tfe_op_attrs_internal.h"],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        ":abstract_op_attrs",
        "//tensorflow/c:conversion_macros",
        "//tensorflow/c:tf_status",
        "//tensorflow/core:protos_all_cc",
    ],
)

cc_library(
    name = "tfe_op_internal",
    hdrs = ["tfe_op_internal.h"],

# addition to this core set of ops, user libraries which are loaded (via
# TF_LoadLibrary/tf.load_op_library) register their ops and kernels with this
# shared object directly.
#
# For example, from Python tf.load_op_library loads a custom op library (via
# dlopen() on Linux), the library finds libtensorflow_framework.so (no

    *   Added 8-bit and 16-bit support for `floor_div` and `floor_mod`.
    *   Added 16-bit and 32-bit int support for the built-in op `bitcast`.
    *   Added 8-bit/16-bit/32-bit int/uint support for the built-in op `bitwise_xor`
    *   Added int16 indices support for built-in op `gather` and `gather_nd`.
    *   Added 8-bit/16-bit/32-bit int/uint support for the built-in op `right_shift`

inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack

build:mkl_aarch64_threadpool -c opt

# CUDA: This config refers to building CUDA op kernels with nvcc.
build:cuda --repo_env TF_NEED_CUDA=1
build:cuda --crosstool_top=@local_config_cuda//crosstool:toolchain
build:cuda --@local_config_cuda//:enable_cuda

# CUDA: This config refers to building CUDA op kernels with clang.
build:cuda_clang --config=cuda