// Return a shape op fetching the shape of `a`.
TFE_Op* ShapeOp(TFE_Context* ctx, TFE_TensorHandle* a);

// Return an allreduce op adding up input tensor `in` from `group_size` workers.
TFE_Op* AllReduceOp(TFE_Context* ctx, TFE_TensorHandle* in, int group_size);

// Return a SendOp op `op_name` with send input tensor `in` and attributes
// `send_device`, `recv_device`, and `send_device_incarnation` set.

                                    <label for="input" class="col-sm-3 text-sm-right col-form-label"><la:message
                                            key="labels.dict_stopwords_source"/></label>
                                    <div class="col-sm-9">
                                        <la:errors property="input"/>
                                        <la:text styleId="input" property="input"

                    "requestBody": {
                        "content": {
                            "application/json": {
                                "schema": {"$ref": "#/components/schemas/Item-Input"}
                            }
                        },
                        "required": True,
                    },
                    "responses": {
                        "200": {

		tgt = subSystemValue[1]
	}

	return subSys, inputs, tgt, e
}

// kvFields - converts an input string of form "k1=v1 k2=v2" into
// fields of ["k1=v1", "k2=v2"], the tokenization of each `k=v`
// happens with the right number of input keys, if keys
// input is empty returned value is empty slice as well.
func kvFields(input string, keys []string) []string {
	valueIndexes := make([]int, 0, len(keys))
	for _, key := range keys {

                    "loc": ["header", "x-token"],
                    "msg": "Field required",
                    "input": None,
                },
                {
                    "type": "missing",
                    "loc": ["header", "x-key"],
                    "msg": "Field required",
                    "input": None,
                },
            ]
        }
    ) | IsDict(

	wantTrailers := make(map[string]struct{}, len(cr.trailers))
	for k := range cr.trailers {
		wantTrailers[strings.ToLower(k)] = struct{}{}
	}
	input := bufio.NewScanner(bytes.NewReader(valueBuffer.Bytes()))
	for input.Scan() {
		line := strings.TrimSpace(input.Text())
		if line == "" {
			continue
		}
		// Find first separator.
		idx := strings.IndexByte(line, trailerKVSeparator[0])

    }

    @Override
    public String getDocumentBoost() {
        return crawlingConfig.getDocumentBoost();
    }

    @Override
    public String getIndexingTarget(final String input) {
        return crawlingConfig.getIndexingTarget(input);
    }

    @Override
    public String getConfigId() {
        return crawlingConfig.getConfigId();
    }

    @Override
    public Integer getTimeToLive() {

        {
            "detail": [
                {
                    "type": "int_parsing",
                    "loc": ["body", "foo", "[key]"],
                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "foo",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [

    TFE_TensorHandle* input = TFE_OpGetFlatInput(original_op, j, s);
    if (TF_GetCode(s) != TF_OK) return;
    const char* input_device = TFE_TensorHandleDeviceName(input, s);
    if (TF_GetCode(s) != TF_OK) return;
    if (dev->device_name == input_device) {
      LoggedTensor* t = reinterpret_cast<LoggedTensor*>(
          TFE_TensorHandleDevicePointer(input, s));
      if (TF_GetCode(s) != TF_OK) return;

labels: ["pkgsite"]
body:
  - type: markdown
    attributes:
      value: "Please answer these questions before submitting your issue. Thanks!"
  - type: input
    id: url
    attributes:
      label: "What is the URL of the page with the issue?"
    validations:
      required: true
  - type: input
    id: user-agent
    attributes:
      label: "What is your user agent?"