compiling from source placeholder: validations: required: false

-   type: textarea id: what-happened attributes: label: Current Behaviour?
    description: Also tell us, what did you expect to happen? placeholder: Tell
    us what you see! value: "A bug happened!" render: shell validations:
    required: true

-   type: textarea id: code-to-reproduce attributes: label: Standalone code to

}

namespace {

struct SomeThreadData {
  ::tensorflow::mutex mu;
  bool did_work = false;
};

void SomeThreadFunc(void* data) {
  auto* real_data = static_cast<SomeThreadData*>(data);
  ::tensorflow::mutex_lock l(real_data->mu);
  real_data->did_work = true;
}

}  // namespace

TEST(TestEnv, TestThreads) {
  TF_ThreadOptions options;

      label: GPU model and memory
      description: If compiling from source
  - type: textarea
    id: what-happened
    attributes:
      label: Current behavior?
      description: Also tell us, what did you expect to happen?
      placeholder: Tell us what you see!
    validations:
      required: true
  - type: textarea
    id: code-to-reproduce
    attributes:

    simplegeneric six Tempita traitlets uritemplate urllib3 virtualenv wcwidth \
    wheel win-unicode-console;

# Hardcoding Android license since I did not find any solution on accepting it
# through the docker build command. If the licensing agreement changes, this 
# will need to be updated as well.
RUN New-Item -ItemType Directory -Path C:\Android\android-sdk\licenses; \

ResultDictType = Dict[str, Dict[str, Union[str, int]]]

RESULT_STORE_LINK_RE = re.compile(
    r'^INFO: Streaming build results to: (https://[\w./\-]+)')
FAILED_BUILD_LINE = 'FAILED: Build did NOT complete successfully'
BUILD_STATUS_LINE = 'INFO: Build'
TESTS_FAILED_RE = re.compile(r'^INFO: Build completed, \d+ tests? FAILED')
BAZEL_COMMAND_RE = re.compile(
    r'(^| )(?P<command>bazel (.*? )?(?P<type>test|build) .+)')

  if (op_reg_data->shape_inference_fn == nullptr) {
    status->status =
        InvalidArgument("No shape inference function exists for op '",
                        node_def.op(), "', did you forget to define it?");
    return;
  }

  status->status = c.Run(op_reg_data->shape_inference_fn);
  if (!status->status.ok()) return;

  // Set output_shapes.

  components.reserve(underlying_devices_.size());

  if (values.size() != num_underlying_devices()) {
    TF_SetStatus(
        status, TF_INVALID_ARGUMENT,
        "Number of values did not match number of underlying devices.");
    return nullptr;
  }
  TF_DataType datatype_enum(
      static_cast<TF_DataType>(DataTypeToEnum<DataType>().value));

  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  // Delete the gradient func.
  // It is safe to delete after adding a copy to host graph.
  TF_DeleteFunction(grad_func);

  // Check that GraphDef did not change
  GraphDef gdef2;
  GetGraphDef(host_graph_, &gdef2);
  ASSERT_EQ(gdef.DebugString(), gdef2.DebugString());

  // Use and run func
  TF_Operation* func_feed = Placeholder(host_graph_, s_);

        quantized models.
    *   Added support for converting and resizing models with dynamic
        (placeholder) dimensions. Previously, there was only limited support for
        dynamic batch size, and even that did not guarantee that the model could
        be properly resized at runtime.
        *   Enabled experimental support for a new quantization mode with 16-bit
            activations and 8-bit weights. See