1.  Make sure the Issue Template is filled out.
1.  The issue should be related to the repo it is created in.

**Here's why we have this policy:** We want to focus on the work that benefits
the whole community, e.g., fixing bugs and adding features. Individual support
should be sought on Stack Overflow or other non-GitHub channels. It helps us to

# Run any CI script and env configuration to bisect a failing target in some
# build configuration. You must set the following variables to control this
# script:
#
#   TF_BISECT_GOOD: Last known good commit (e.g. commit from the last passing job)
#   TF_BISECT_BAD: First bad commit (e.g. commit from the first failing job)
#   TF_BISECT_SCRIPT: The build script path relative to the TF root dir, e.g.

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.

The `_api/v2/api_packages.txt` file is generated during the TensorFlow API v2
init files creation process and is subsequently stored in the wheel file after

    if [[ "$(git rev-parse --abbrev-ref HEAD)" == "pull_branch" ]]; then
        # TF's CI runs 'git fetch origin "pull/PR#/merge:pull_branch"'
        # To get the as-merged branch during the CI tests
        git diff --diff-filter ACMRT --name-only pull_branch^ pull_branch > $BATS_FILE_TMPDIR/changed_files
    else
        # If the branch is not present, then diff against origin/master

This is a Python test that verifies whether API v2 packages can be imported
from the current build or not.

It uses the `_api/v2/api_packages.txt` file from the local wheel file.
The `_api/v2/api_packages.txt` file is created during the process of generating
TensorFlow API v2 init files and is stored in the wheel file after the build.

See README.md file for "how to run" instruction.
"""

import logging
import unittest
import pkg_resources

#     avx_win:                Build with avx instruction set on windows
#
# Other build options:
#     short_logs:       Only log errors during build, skip warnings.
#     verbose_logs:     Show all compiler warnings during build.
#     monolithic:       Build all TF C++ code into a single shared object.
#     dynamic_kernels:  Try to link all kernels dynamically (experimental).

---
name: TensorFlow Lite Converter Issue
about: Use this template for reporting issues during model conversion to TFLite
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

void TF_InitGraph(TP_OptimizerRegistrationParams* params, TF_Status* status);

// Get a set of node names that must be preserved. They can not be transformed
// or removed during the graph transformation. This includes feed and fetch
// nodes, keep_ops, init_ops. Fills in `num_values` and `storage_size`, they
// will be used in `TF_GetNodesToPreserveList`.

  // Rename local device
  // This server def has the task index set to 0.
  string serialized = server_def.SerializeAsString();
  TFE_ContextSetServerDef(ctx, 0, serialized.data(), serialized.size(), status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  const string dev1_name =
      absl::StrCat("/job:", first_name, "/replica:0/task:0/device:CPU:0");

  new value.

  Args:
    environ_cp: (Dict) copy of the os.environ.
    var_name: (String) string for name of environment variable, e.g. "TF_MYVAR".
    var_default: (String) default value string.
    ask_for_var: (String) string for how to ask for user input.
    check_success: (Function) function that takes one argument and returns a