scalar_mul(dilation_rate_value, scalar_sub(filter_size, one)), one);

  // output_size = (input_size + stride - 1) / stride
  Value output_size = scalar_div(
      scalar_add(input_size, scalar_sub(stride_value, one)), stride_value);
  // padding_needed = std::max(
  //     0,
  //     (output_size - 1) * stride + effective_filter_size - input_size)
  Value padding_needed = scalar_sub(
      scalar_add(effective_filter_size_op,

      }
    }
  }
  return found_dtype && found_value;
}

bool IsAddN(const tensorflow::NodeDef& node_def, int n) {
  if (node_def.op() != "AddN" || node_def.name() != "add" ||
      node_def.input_size() != n) {
    return false;
  }
  bool found_t = false;
  bool found_n = false;
  for (const auto& attr : node_def.attr()) {
    if (attr.first == "T") {

StatusOr<string> GetVarHandleName(
    const std::unordered_map<string, NodeDef*>& name_to_node_map,
    string node_name) {
  const NodeDef* node = name_to_node_map.at(node_name);
  while (node->input_size() > 0) {
    auto parent = name_to_node_map.find(node->input(0));
    if (parent == name_to_node_map.end()) break;
    node = parent->second;
    if (node->op() != "Identity") {

                        std::vector<const Edge*> control_deps, Output* size) {
  // If slice_size[i] >= 0 then slice_size[i] = slice_size[i].
  //
  // If slice_size[i] == -1 then slice_size[i] = input_size[i] -
  // begin[i].
  //
  // If slice_size[i] < -1 then executing the slice will throw an error, and we
  // don't do anything here.  We've already filtered these cases out in
  // IsRewritableSlice.

// CHECK:  [[ZERO_1:%.*]] = arith.constant dense<0> : tensor<i32>
// CHECK:  [[INPUT_SIZE:%.*]] = "tf.Gather"([[SHAPE]], [[ZERO_1]]) <{validate_indices = true}> : (tensor<2xi32>, tensor<i32>) -> tensor<i32>
// CHECK:  [[SIZE_DIFF:%.*]] = "tf.Sub"([[SIZE]], [[INPUT_SIZE]]) : (tensor<i32>, tensor<i32>) -> tensor<i32>

      // $filter_sizes
      CompileTimeConstantOperand<TF::Conv2DBackpropFilterOp, 1>,
      CompileTimeConstantOperand<TF::Conv2DBackpropInputOp, 0>,  // $input_sizes
      // $filter_sizes
      CompileTimeConstantOperand<TF::Conv3DBackpropFilterV2Op, 1>,
      // $input_sizes
      CompileTimeConstantOperand<TF::Conv3DBackpropInputV2Op, 0>,
      // $group_assignment
      CompileTimeConstantOperand<TF::CrossReplicaSumOp, 1>,

def LegalizeConv2DBackpropInput : Pat<
  (TF_Conv2DBackpropInputOp $input_sizes, $filter, $out_backprop,
     IsIntList1XY1:$strides,
     BoolAttr:$use_cudnn_on_gpu,
     IsSameOrValid:$padding,
     I64ArrayAttr:$explicit_paddings,
     IsDataFormatNHWC:$data_format,
     IsAllOnes:$dilations),
  (TFL_TransposeConvOp $input_sizes,
     (TFL_TransposeOp $filter,

	defer runtime.GOMAXPROCS(runtime.GOMAXPROCS(1))
	x := rndNat(50000)
	y := rndNat(40)
	allocSize := allocBytes(func() {
		nat(nil).mul(x, y)
	})
	inputSize := uint64(len(x)+len(y)) * _S
	if ratio := allocSize / uint64(inputSize); ratio > 10 {
		t.Errorf("multiplication uses too much memory (%d > %d times the size of inputs)", allocSize, ratio)
	}
}

        if (StringUtil.isBlank(command)) {
            throw new CrawlerSystemException("command is empty.");
        }

        final Map<String, String> params = new HashMap<>();
        params.put("$INPUT_FILE", inputFile.getAbsolutePath());
        params.put("$OUTPUT_FILE", outputFile.getAbsolutePath());

        final List<String> cmdList = parseCommand(command, params);
        if (logger.isInfoEnabled()) {

        private IndexRoot root;

        @Override
        protected byte getType() {
            return 0x77;
        }

        @Override
        protected int getSize() {
            return Block.INT_SIZE + Block.LONG_SIZE + (3 * Block.LONG_SIZE) * maxChildIndexEntries;
        }

        @Override
        public void read(DataInputStream instr) throws IOException {
            int count = instr.readInt();