-	 ? (__builtin_constant_p (s2) && __string2_1bptr_p (s2)		      \
-	    ? __builtin_strcmp (s1, s2)					      \
-	    : __strcmp_cg (s1, s2, __s1_len))				      \
-	 : (__builtin_constant_p (s2) && __string2_1bptr_p (s2)		      \
-	    && (__s2_len = strlen (s2), __s2_len < 4)			      \
-	    ? (__builtin_constant_p (s1) && __string2_1bptr_p (s1)	      \
-	       ? __builtin_strcmp (s1, s2)				      \

## Wheel Test

This directory is dedicated to tests that require a built TensorFlow wheel
file for testing, such as:

* Ensuring the entire API is importable
* Testing downstream projects against the wheel

Ensure you have Bazel installed and accessible from your command line.

These tests use hermetic Python. They also require a built TensorFlow wheel file
and a requirements_lock file. The requirements_lock file is generated by the

# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the
# > target platform.
# See also https://docs.bazel.build/versions/master/guide.html#build-configurations-and-cross-compilation
build --distinct_host_configuration=false

  TF_AbstractFunction* func =
      TF_FinalizeFunction(graph_ctx, mm_outputs, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  /* Now that the graph is built, test graph implementation on matmul example:
    [[1,1] ,   *   [[1,1] ,   =  [[2,2],
     [1,1]]         [1,1]]        [2,2]]
  */

  // Build eager context.
  TFE_ContextOptions* opts = TFE_NewContextOptions();

        "//tensorflow/lite/core/api",
        "//tensorflow/lite/delegates/flex:delegate",
        "//tensorflow/lite/kernels/internal:compatibility",
        "//tensorflow/lite/kernels:builtin_ops",
        "//tensorflow/lite/kernels:reference_ops",
        "//tensorflow/lite/schema:schema_fbs",
        "//tensorflow/lite/toco/logging:conversion_log_util",

  return platform.system().startswith('CYGWIN_NT')


def get_input(question):
  try:
    try:
      answer = raw_input(question)
    except NameError:
      answer = input(question)  # pylint: disable=bad-builtin
  except EOFError:
    answer = ''
  return answer


def symlink_force(target, link_name):
  """Force symlink, equivalent of 'ln -sf'.

  Args:
    target: items to link to.

    *   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`

# A tape built on top of unified execution APIs.
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

package(
    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
    licenses = ["notice"],
)

cc_library(
    name = "tape_context",
    srcs = ["tape_context.cc"],
    hdrs = [
        "tape_context.h",
    ],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [

labels: 'TFLiteConverter'

---

### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installation (pip package or built from source):
- TensorFlow library (version, if pip package or github SHA, if built from source):

### 2. Code

Provide code to help us reproduce your issues using one of the following options:

#### Option A: Reference colab notebooks

# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the
# > target platform.
# See also https://docs.bazel.build/versions/master/guide.html#build-configurations-and-cross-compilation
build --distinct_host_configuration=false