"""
# pylint: disable=g-bad-import-order,protected-access,g-import-not-at-top

import distutils as _distutils
import importlib
import inspect as _inspect
import os as _os
import site as _site
import sys as _sys

# Do not remove this line; See https://github.com/tensorflow/tensorflow/issues/42596

// Fetches the current number of inputs attached to `op`.
//
// Does not use the operation's definition to determine how many inputs should
// be attached. It is intended for use with TFE_OpGetFlatInput to inspect an
// already-finalized operation.
//
// Note that TFE_OpGetFlatInputCount and TFE_OpGetFlatInput operate on a flat
// sequence of inputs, unlike TFE_OpGetInputLength (for getting the length of a

// ParallelDevice.
class ParallelTensor {
 public:
  // Construct a ParallelTensor from TensorHandles placed on the component
  // devices of a ParallelDevice. If called, ParallelTensor::Shape inspects
  // `components` to determine a shape.
  static std::unique_ptr<ParallelTensor> FromTensorHandles(
      const ParallelDevice& parallel_device,
      std::vector<TensorHandlePtr> components, TF_Status* status);

      register_test_pass(tensorflow::OptimizationPassRegistry::PRE_PLACEMENT, 0,
                         std::make_unique<GraphErrorInjectionPass>(),
                         "error_injector");
  GraphErrorInjectionPass::enabled_ = true;

  tensorflow::ServerDef server_def = GetServerDef(3);
  // This server def has the task index set to 0.
  string serialized = server_def.SerializeAsString();

# ==============================================================================
"""Bring in all of the public TensorFlow interface into this module."""

import distutils as _distutils
import importlib
import inspect as _inspect
import os as _os
import site as _site
import sys as _sys

# pylint: disable=g-bad-import-order,protected-access,g-import-not-at-top
from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import

### Typical Pull Request Workflow -

**1. New PR**

- As a contributor, you submit a New PR on GitHub.
- We inspect every incoming PR and add certain labels to the PR such as `size:`,
  `comp:` etc.  At this stage we check if the PR is valid and meets certain
  quality requirements. For example, we check if the CLA is signed, PR has

// APIs for generically dealing with op attributes (e.g. when forwarding them
// through custom device implementations).
//
// TODO(allenl): Currently these are black boxes, but we should have some way to
// inspect values. This would let people e.g. copy over most attributes and then
// modify some based on their values.

// A reference to an op's name -> attribute mapping
typedef struct TFE_OpAttrs TFE_OpAttrs;

    docker push "$TFCI_DOCKER_IMAGE"
  fi
fi

# Keep the existing "tf" container if it's already present.
# The container is not cleaned up automatically! Remove it with:
# docker rm tf
if ! docker container inspect tf >/dev/null 2>&1 ; then
  # Pass all existing TFCI_ variables into the Docker container
  env_file=$(mktemp)
  env | grep ^TFCI_ > "$env_file"
  docker run $TFCI_DOCKER_ARGS --name tf -w "$TFCI_GIT_DIR" -itd --rm \

vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.
Eager mode lets users write imperative-style statements that can be easily
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

        should be set to `True` when using imperative control flow in the `call`
        method.
    *   Begin adding Go wrapper for C Eager API.
    *   XLA HLO graphs can be inspected with interactive_graphviz tool now.
    *   Add dataset ops to the graph (or create kernels in Eager execution)
        during the python Dataset object creation instead doing it during
        Iterator creation time.