ASSERT_EQ(-2147483648, op.getLhsQuantizationMinValAttr().getInt());
  ASSERT_EQ(2147483647, op.getRhsQuantizationMaxValAttr().getInt());
  ASSERT_EQ(-2147483648, op.getRhsQuantizationMinValAttr().getInt());
  ASSERT_EQ(2147483647, op.getOutputQuantizationMaxValAttr().getInt());
  ASSERT_EQ(-2147483648, op.getOutputQuantizationMinValAttr().getInt());

        this.flags2 = Config.getInt(p, "jcifs.smb.client.flags2", 0);

        this.capabilities = Config.getInt(p, "jcifs.smb.client.capabilities", 0);

        this.sessionLimit = Config.getInt(p, "jcifs.smb.client.ssnLimit", SmbConstants.DEFAULT_SSN_LIMIT);

        this.maxRequestRetries = Config.getInt(p, "jcifs.smb.client.maxRequestRetries", 2);

  if (mlir::cast<IntegerAttr>(elements.front()).getInt() != 1 ||
      mlir::cast<IntegerAttr>(elements.back()).getInt() != 1)
    return false;

  Builder b(op->getContext());
  *x = b.getI32IntegerAttr(mlir::cast<IntegerAttr>(elements[1]).getInt());
  *y = b.getI32IntegerAttr(mlir::cast<IntegerAttr>(elements[2]).getInt());
  *z = b.getI32IntegerAttr(mlir::cast<IntegerAttr>(elements[3]).getInt());

  return true;
}

                    value.removeIf(i -> i.getAlias().equals(importedClass.getAlias()));
                } catch (Exception e) {
                    throw UncheckedException.throwAsUncheckedException(e);
                }
            }
        }

        iter = source.getAST().getStaticImports().values().iterator();
        while (iter.hasNext()) {

      auto get_int = [](Attribute attr) {
        return mlir::cast<IntegerAttr>(attr).getInt();
      };

      SmallVector<int32_t> padding_values(padding_attr_array.size());
      for (int i = 0; i < padding_attr_array.size(); i++) {
        padding_values[i] =
            static_cast<int32_t>(get_int(padding_attr_array[i]));
      }

  return type && type.getRank() == rank &&
         mlir::isa<FloatType>(type.getElementType());
}

// Returns true if the given `value` has the specified rank or has unranked
// type.
inline bool IsOfRankOrUnranked(Value value, int64_t rank) {
  RankedTensorType type = GetRankedTensorTypeForOperand(value);
  return !type || type.getRank() == rank;
}

     * @throws Exception
     */
    @Test
    public void testGetLast() throws Exception {
        try {
            list.getLast();
            fail();
        } catch (final NoSuchElementException ex) {
            System.out.println(ex);
        }
        list.addLast("1");
        assertThat(list.getLast(), is("1"));
    }

    /**
     * @throws Exception
     */
    @Test

        return (constant.getType().getRank() == 2);
      };

  auto op = cast<BatchMatMulOpType>(bmm_op);

  // Create a tfl.transpose op that performs ZX transpose on `input`.
  auto create_z_x_transpose_op = [&](Value input) -> Value {
    RankedTensorType input_type = mlir::cast<RankedTensorType>(input.getType());
    const int input_rank = input_type.getRank();

    ShapedType& updates_type, ConversionPatternRewriter& rewriter) {
  auto canonical_update_window_dims = llvm::to_vector(
      llvm::seq<int64_t>(indices_type.getRank() - 1, updates_type.getRank()));

  if (canonical_update_window_dims == update_window_dims) return success();

  // Permute updates if `update_window_dims` are leading indices.

  for (auto idx : val1_indices) {
    if (idx < 0) idx = idx + val1_shape.getRank();
    if (idx >= val1_shape.getRank() || val1_shape.isDynamicDim(idx)) {
      return false;
    }
    val1_result *= val1_shape.getDimSize(idx);
  }

  for (auto idx : val2_indices) {
    if (idx < 0) idx = idx + val2_shape.getRank();
    if (idx >= val2_shape.getRank() || val2_shape.isDynamicDim(idx)) {
      return false;
    }