TF_ImportGraphDefResults* results, int* num_opers, TF_Operation*** opers);

// Fetches any input mappings requested via
// TF_ImportGraphDefOptionsAddInputMapping() that didn't appear in the GraphDef
// and weren't used as input to any node in the imported graph def. The number
// of fetched mappings is returned in `num_missing_unused_input_mappings`. The

      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:

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) .+)')

  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));

    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

  // 1) nullptr indicates tensor is compact and row-majored.
  // 2) fill in the strides array as the real case for compact row-major data.
  // Here we choose option 2, since some frameworks didn't handle the strides
  // argument properly.
  dlm_tensor->dl_tensor.strides = stride_arr->data();

  dlm_tensor->dl_tensor.byte_offset =
      0;  // TF doesn't handle the strides and byte_offsets here

//   * allows having older plugins than the current core TensorFlow (though
//     we are still warning users): the entries that core TensorFlow expects
//     but plugins didn't provide will be set to `nullptr` values and core
//     TensorFlow will know to not call these on behalf of users;
//   * increased security as plugins will not be able to alter function table

  reasonable contribution (meaning it is not a single liner cosmetic PR).

**2. Valid?**

-   If the PR passes all the quality checks then we go ahead and assign a
    reviewer.
-   If the PR didn't meet the validation criteria, we request for additional
    changes to be made to PR to pass quality checks and send it back or on a
    rare occasion we may reject it.

**3. Review**

    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; \

  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.