TF_NewTensor(TF_FLOAT, nullptr, 0, values, num_bytes, &FloatDeallocator,
                   nullptr),
      TF_DeleteTensor);
  return TensorHandlePtr(TFE_NewTensorHandle(tensor.get(), status));
}

// Creates a rank-one TFE_TensorHandle with value `v`.
TensorHandlePtr VectorFloatTensorHandle(const std::vector<float>& v,
                                        TF_Status* status) {
  const int num_bytes = v.size() * sizeof(float);

        as `tf.tpu.experimental.embedding.serving_embedding_lookup` which can
        take arbitrary rank of dense and sparse tensor. For ragged tensor,
        though the input tensor remains to be rank 2, the activations now can be
        rank 2 or above by specifying the output shape in the feature config or
        via the build method.

*   Add

    TFE_Context* ctx, const char* task, int64_t timeout_in_ms,
    TF_Status* status);

// Information about the shape of a Tensor and its type.
struct TF_ShapeAndType {
  // Number of dimensions. -1 indicates unknown rank.
  int num_dims;
  // Array of dimensions. -1 indicates unknown dim.
  int64_t* dims;
  // The data type. May be 0 to denote unknown type.
  TF_DataType dtype;
};

    TF_RETURN_IF_ERROR(
        operation->SetOpName(absl::StrCat("_input_", inputs_.size()).c_str()));
    TF_RETURN_IF_ERROR(operation->SetAttrType("dtype", dtype));
    if (!shape.unknown_rank()) {
      TF_RETURN_IF_ERROR(operation->SetAttrShape(
          "shape", reinterpret_cast<int64_t*>(shape.dim_sizes().data()),
          shape.dims()));
    }
    int num_outputs = 1;

        return errors::Unimplemented(absl::StrCat(
            "Computing the shape of a ParallelTensor when the components do "
            "not all have the same rank is not supported. One tensor had "
            "shape ",
            first_shape.DebugString(), " and another had shape ",
            component_shape.DebugString()));
      } else {

                                              const char* attr_name,
                                              const char* value, size_t length);

// Set `num_dims` to -1 to represent "unknown rank".  Otherwise,
// `dims` points to an array of length `num_dims`.  `dims[i]` must be
// >= -1, with -1 meaning "unknown dimension".
TF_CAPI_EXPORT extern void TF_SetAttrShape(TF_OperationDescription* desc,

// required except where indicated.
typedef struct TFE_CustomDeviceTensorHandleMethods {
  int version = TFE_CUSTOM_DEVICE_VERSION;

  // Computes the rank of the tensor handle.
  //
  // Shapes are specified via callbacks because retrieving the shape of a tensor
  // is a blocking operation for async eager; custom devices should avoid

  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ(dims[0], returned_dims[0]);
  EXPECT_EQ(dims[1], returned_dims[1]);

  // Try to set 'unknown' with unknown rank on the shape and see that
  // it doesn't change.
  TF_GraphSetTensorShape(graph, feed_out_0, /*dims=*/nullptr, -1, s);
  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

                        static_cast<TF_DataType>(default_value.type()));
      break;
    case tensorflow::AttrValue::kShape: {
      const auto& tensor_shape = default_value.shape();
      if (tensor_shape.unknown_rank()) {
        TFE_OpSetAttrShape(op, attr_name, nullptr, -1, status);
      } else {
        const auto num_dims = tensor_shape.dim_size();
        std::unique_ptr<int64_t[]> dims(new int64_t[num_dims]);

					<groupId>org.opensearch.plugin</groupId>
					<artifactId>aggs-matrix-stats-client</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>rank-eval-client</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>lang-expression</artifactId>
				</exclusion>
				<exclusion>