import (
	"internal/abi"
	"unsafe"
)

// Should be a built-in for unsafe.Pointer?
//
// add should be an internal detail,
// but widely used packages access it using linkname.
// Notable members of the hall of shame include:
//   - fortio.org/log
//
// Do not remove or change the type signature.
// See go.dev/issue/67401.
//
//go:linkname add
//go:nosplit
func add(p unsafe.Pointer, x uintptr) unsafe.Pointer {

  StringRef getArgument() const final {
    return "quant-duplicate-shape-determining-constants";
  }

  StringRef getDescription() const final {
    return "Duplicates shape-determining constants. A shape-determining "
           "constant is a constant that are transitively used to change or "
           "determine the shape of a tensor. For example, the second argument "

  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  const std::vector<std::unique_ptr<ParallelTensor>>& handles = *outputs;
  const std::vector<int64_t>* shape;
  Status s = handles[0]->Shape(&shape);
  ASSERT_TRUE(s.ok());
  EXPECT_EQ(0, shape->size());
}

TEST(PARALLEL_DEVICE_LIB, TestCancelOnError) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);

                this.canon = "/" + this.share + "/" + StringUtil.join("/", remainParts) + ( trailingSlash ? "/" : "" );
            }
            else {
                this.unc = "\\";
                if ( this.share != null ) {
                    this.canon = "/" + this.share + ( trailingSlash ? "/" : "" );
                }
                else {

      std::vector<int64_t> shape;
      shape.push_back(static_cast<int64_t>(data.size()));
      attributes.emplace_back(builder.getNamedAttr(
          "window_strides", BuildVhloTensorV1Attr(shape, data, builder)));
    }
    if (!(op->padding.empty())) {
      std::vector<int64_t> shape;
      shape.push_back(static_cast<int64_t>(op->padding.size()) / 2);
      shape.push_back(2);

	{`c:/`, `c:/`, ``},
	{`c:/foo`, `c:/`, `foo`},
	{`c:/foo/bar`, `c:/foo/`, `bar`},
	{`//host/share`, `//host/share`, ``},
	{`//host/share/`, `//host/share/`, ``},
	{`//host/share/foo`, `//host/share/`, `foo`},
	{`\\host\share`, `\\host\share`, ``},
	{`\\host\share\`, `\\host\share\`, ``},
	{`\\host\share\foo`, `\\host\share\`, `foo`},
}

func TestSplit(t *testing.T) {
	var splittests []SplitTest

            tensor_spec.TensorSpec(
                shape=shape, dtype=dtypes.float32, name='input_tensor'
            )
        ),
    )
    return model

  # Prepares sample einsum input data shapes.
  # This function returns:
  # 1. Shape for input 1
  # 2. Shape for input 2
  # 3. Shape for bias
  # 4. Signature for input 1 (Could contain None dimension)

}

// Gets the shape of operand, assuming it is a dynamic shape with static rank.
Value getShapeValue(Location loc, Value operand, PatternRewriter &rewriter) {
  RankedTensorType resultType =
      mlir::dyn_cast<RankedTensorType>(operand.getType());
  return rewriter.create<shape::ShapeOfOp>(
      loc,
      RankedTensorType::get(/*shape=*/{resultType.getRank()},

				&field.Error{
					Type:  field.ErrorTypeInvalid,
					Field: "shape[0].utilization",
				},
				&field.Error{
					Type:  field.ErrorTypeInvalid,
					Field: "shape[1].score",
				},
				&field.Error{
					Type:  field.ErrorTypeInvalid,
					Field: "shape[2].score",
				},
				&field.Error{
					Type:  field.ErrorTypeInvalid,
					Field: "shape[3].utilization",
				},
			}),
		},
	}

  auto value_type = mlir::cast<RankedTensorType>(value.getType());
  auto shape = value_type.getShape();
  SmallVector<int64_t, 4> transposed_shape(shape.begin(), shape.end());
  for (int i = 0, end = shape.size(); i < end; ++i) {
    transposed_shape[i] = shape[permutation[i]];
  }
  auto transposed_type =
      RankedTensorType::get(transposed_shape, value_type.getElementType());