static final int LPORT = Config.getInt( "jcifs.smb1.smb.client.lport", 0 );
    static final int MAX_MPX_COUNT = Config.getInt( "jcifs.smb1.smb.client.maxMpxCount", DEFAULT_MAX_MPX_COUNT );
    static final int SND_BUF_SIZE = Config.getInt( "jcifs.smb1.smb.client.snd_buf_size", DEFAULT_SND_BUF_SIZE );
    static final int RCV_BUF_SIZE = Config.getInt( "jcifs.smb1.smb.client.rcv_buf_size", DEFAULT_RCV_BUF_SIZE );

        int andRecordCount = JsonPath.from(andResponse).getInt("record_count");

        Map<String, String> params = new HashMap<>();
        params.put("q", query);
        params.put("num", "100");
        String response = checkMethodBase(new HashMap<>()).params(params).get("/api/v1/documents").asString();
        assertTrue(JsonPath.from(response).getInt("record_count") > andRecordCount);

    auto reshape_input_ty = mlir::dyn_cast<ShapedType>(reshape_input.getType());
    if (!reshape_input_ty.hasStaticShape() || input_ty.getRank() == 0 ||
        reshape_input_ty.getRank() == 0 ||
        input_ty.getDimSize(input_ty.getRank() - 1) !=
            reshape_input_ty.getDimSize(reshape_input_ty.getRank() - 1)) {
      return failure();
    }

    // Connect the input to the one of reshape.

  auto operand_shape = builder.create<TFL::ShapeOp>(
      RankedTensorType::get(static_cast<int32_t>(operand_type.getRank()),
                            builder.getIntegerType(32)),
      operand);
  const int64_t operand_rank = operand_type.getRank();
  // Compute flattened out dimension and contracting dimension using
  // TFL::UnsortedSegmentProdOp.

	if err != nil {
		return si, err
	}
	return si, nil
}

// Gets a protocol from global var protocols
func getProtocolInstance(f *fuzz.ConsumeFuzzer) (protocol.Instance, error) {
	pIndex, err := f.GetInt()
	if err != nil {
		return protocol.Unsupported, errors.New("could not create protocolInstance")
	}
	i := protocols[pIndex%len(protocols)]
	return i, nil
}

// Creates a new fuzzed Port

            (getElementTypeOrSelf(op.getOutput().getType()))))
      return failure();

    ElementsAttr input_tensor = qconst_op.getValue();

    assert(perm_tensor.getType().getRank() == 1);
    const int num_dimensions = input_tensor.getShapedType().getRank();
    assert(perm_tensor.getType().getNumElements() == num_dimensions);

    ArrayRef<int64_t> input_shape = input_tensor.getShapedType().getShape();

  if (!dims_type) return success();
  if (dims_type.getRank() > 1)
    return emitError(loc, "dimensions can only be 0D or 1D tensor");

  auto input_type = mlir::dyn_cast<RankedTensorType>(input.getType());
  if (!input_type) return success();
  int64_t rank = input_type.getRank();

  DenseIntElementsAttr dims_attr;
  if (!matchPattern(dims, m_Constant(&dims_attr))) return success();

  const int64_t input_rank =
      mlir::dyn_cast<ShapedType>(dot_general_op.getOperand(0).getType())
          .getRank();
  const int64_t filter_rank =
      mlir::dyn_cast<ShapedType>(dot_general_op.getOperand(1).getType())
          .getRank();
  // The following conditions are such requirements:
  //   - rank(lhs) is 1 or 2
  //   - rank(rhs) = 2
  //   - size(lhs_contracting_dimensions) = 1

  if (!producer) return mlir::failure();
  versions->set_producer(producer.getInt());

  auto min_consumer = mlir::dyn_cast_or_null<mlir::IntegerAttr>(
      version_attr.get("min_consumer"));
  if (min_consumer) versions->set_min_consumer(min_consumer.getInt());

  auto bad_consumers = mlir::dyn_cast_or_null<mlir::ArrayAttr>(
      version_attr.get("bad_consumers"));

}

PermutationAndShape GetPermutationAndTransposedShape(
    llvm::ArrayRef<int64_t> permutation_array, ShapedType input_type,
    ConversionPatternRewriter& rewriter) {
  assert(permutation_array.size() == input_type.getRank());
  llvm::SmallVector<int64_t> transposed_shape(permutation_array.size());
  for (int64_t i = 0; i < permutation_array.size(); ++i) {
    transposed_shape[i] = input_type.getDimSize(permutation_array[i]);
  }