TF_CAPI_EXPORT extern void TFE_InferShapes(
    TFE_Op* op, TF_ShapeAndTypeList* input_shapes, TF_Tensor** input_tensors,
    TF_ShapeAndTypeList* input_tensor_as_shapes,
    TF_ShapeAndTypeList** input_resource_shapes_and_types,
    TF_ShapeAndTypeList** output_shapes,
    TF_ShapeAndTypeList*** output_resource_shapes_and_types, TF_Status* status);

TF_CAPI_EXPORT extern void
TF_ImportGraphDefOptionsSetValidateColocationConstraints(

void TFE_InferShapes(TFE_Op* tfe_op, TF_ShapeAndTypeList* input_shapes,
                     TF_Tensor** input_tensors,
                     TF_ShapeAndTypeList* input_tensors_as_shapes,
                     TF_ShapeAndTypeList** input_resource_shapes_and_types,
                     TF_ShapeAndTypeList** output_shapes,
                     TF_ShapeAndTypeList*** output_resource_shapes_and_types,
                     TF_Status* status) {

                    /*input_tensors_as_shapes*/ nullptr,
                    /*input_resource_shapes_and_types*/ nullptr, &output_shapes,
                    /*output_resource_shapes_and_types*/ nullptr, status_);
    CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
    CHECK_EQ(output_shapes->num_items, 1);

    int num_dims = output_shapes->items[0].num_dims;
    int64_t* dims = output_shapes->items[0].dims;

     * <code>smb://server/</code>, or <code>smb://server/share/</code> URLs
     * because workgroups, servers, and shares cannot be dynamically created
     * (although in the future it may be possible to create shares).
     *
     * @throws CIFSException
     */
    void mkdirs () throws CIFSException;


    /**

		dataDir          string
		data             []byte
		shares           int
		transitionStatus string
		restoreObjStatus string
		expireRestored   bool
		expectedDataDir  string
	}{
		{ // object versions with inlined data don't count towards shared data directory
			versionID: mustGetUUID(),
			dataDir:   d0,
			data:      data,
			shares:    0,
		},

  // run and have results. If is_async=true it means that all of the
  // device-specific executors have scheduled the op.
  //
  // Accepts inferred shapes for outputs (`expected_output_shapes`), which if
  // fully defined will avoid querying the shapes of the underlying
  // TensorHandles when ParallelTensor::Shape is called. This allows async
  // computation to continue without blocking.
  //

                        TF_Status* status) const {
  std::vector<PartialTensorShape> expected_output_shapes(expected_max_outputs);
  StartExecute(context, inputs, operation_name, attributes,
               expected_max_outputs, *default_cancellation_manager_);
  auto result = Join(expected_output_shapes, status);
  if (TF_GetCode(status) != TF_OK) {
    std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> await_status(

        }

        @Override
        public boolean hasNext() {
            return index < STYLES.length;
        }

        @Override
        public DateFormat next() {
            if (!hasNext()) {
                throw new NoSuchElementException();
            }
            final int style = STYLES[index++];
            return DateFormat.getTimeInstance(style, locale);
        }

        @Override

        }

        @Override
        public boolean hasNext() {
            return index < STYLES.length;
        }

        @Override
        public DateFormat next() {
            if (!hasNext()) {
                throw new NoSuchElementException();
            }
            final int style = STYLES[index++];
            return DateFormat.getDateTimeInstance(style, style, locale);
        }

    std::vector<PartialTensorShape> shapes;
    shapes.reserve(num_values);
    for (int i = 0; i < num_values; ++i) {
      if (num_dims[i] < 0) {
        shapes.emplace_back();
      } else {
        shapes.emplace_back(ArraySlice<int64_t>(
            reinterpret_cast<const int64_t*>(dims[i]), num_dims[i]));
      }
    }
    op_->node_builder.Attr(attr_name, shapes);
    return absl::OkStatus();
  }