// Checks if the value has static shape.
def HasStaticShapeConstraint : Constraint<CPred<"HasStaticShape($0)">>;

// Checks if the value has static shape at given dims.
class HasStaticShapeAtDimsConstraint<string dims> : Constraint<
  CPred<"HasStaticShapeAtDims($0, {"# dims #"})">>;

// The rewrite rule cannot replace a value with itself, so we work around
// by cloning the root op to replicate that value. The old op will get folded.

#else
  const char* device_name_ = tensorflow::DEVICE_CPU;
#endif
};

// Validates that the tensor has shape and type corresponding to
// dims and dtype.
void validate_tensor(TF_Tensor* tensor, int64_t* dims, int64_t num_dims,
                     TF_DataType dtype);

// Copies data of length tensor_size_bytes from values to tensor.
template <typename T>

// [b, 0, 1, f]). The weight constant is folded into [0, 1, i, o] format.
// CHECK-DAG: %[[CST:.+]] = stablehlo.constant dense<3.000000e+00> : tensor<3x3x8x8xf32>
// CHECK: %[[TRANSPOSE_1:.+]] = stablehlo.transpose %arg0, dims = [0, 2, 3, 1] : (tensor<1x8x4x4xf32>) -> tensor<1x4x4x8xf32>

TEST(BitcastOpTest, TestImpossibleCast) {
  Tensor int8_input(DT_UINT8, {1});
  TestBitcastOp(&int8_input, DT_UINT32, TensorShape(), error::INVALID_ARGUMENT);
}

PartialTensorShape S(std::initializer_list<int64_t> dims) {
  return PartialTensorShape(dims);
}

TEST(BitcastOpTest, TestShapeInference_LargerShape) {
  const OpRegistrationData* reg;
  TF_CHECK_OK(OpRegistry::Global()->LookUp("Bitcast", &reg));
  OpDef op_def = reg->op_def;

class Operation;

namespace TF {

class AddV2Op;
class SubOp;
class MulOp;
class DivOp;
class RealDivOp;

// Verifies an reduction op's `input` and reduction `dims`.
LogicalResult VerifyReductionInputAndDims(Value input, Value dims,
                                          Location loc);

// A type range with description (in singular form) attached to it.
using TypeRangeWithDesc = std::pair<TypeRange, StringRef>;

  DLManagedTensor* dlm_tensor = &tf_dlm_tensor_ctx->tensor;
  dlm_tensor->manager_ctx = tf_dlm_tensor_ctx;
  dlm_tensor->deleter = &DLManagedTensorDeleter;
  dlm_tensor->dl_tensor.device = tf_dlm_context;
  int ndim = tensor->dims();
  dlm_tensor->dl_tensor.ndim = ndim;
  dlm_tensor->dl_tensor.data = tf_dlm_data;
  dlm_tensor->dl_tensor.dtype = tf_dlm_type;

  std::vector<int64_t>* shape_arr = &tf_dlm_tensor_ctx->shape;

                           const int64_t* dims, const int num_dims,
                           const char* device, TF_Status* status) {
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(
      TFE_NewOp(context, "VarHandleOp", status), TFE_DeleteOp);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op.get(), "dtype", type);
  TFE_OpSetAttrShape(op.get(), "shape", dims, num_dims, status);

    ASSERT_NO_FATAL_FAILURE(CheckTensorValue(outputs[0], dA_vals[i],
                                             /*dims*/ {3, 3},
                                             /*abs_error*/ 0));
    ASSERT_NO_FATAL_FAILURE(CheckTensorValue(outputs[1], dB_vals[i],
                                             /*dims*/ {3, 3},
                                             /*abs_error*/ 0));
    outputs[0]->Unref();
    outputs[1]->Unref();

    return a;
  }
  int64_t rank = a.getRank();
  SmallVector<int64_t, 4> dims;
  dims.resize(rank);
  for (int i = 0, e = rank; i != e; i++) {
    int64_t dim0 = a.getDimSize(i);
    int64_t dim1 = b.getDimSize(i);
    dims[i] = (dim0 == ShapedType::kDynamic) ? dim1 : dim0;
  }
  return RankedTensorType::get(dims, a.getElementType());
}
}  // namespace detail

  static char empty;
  int64_t nelems = 1;
  std::vector<int64_t> dims;
  dims.reserve(shape.dims());
  for (int i = 0; i < shape.dims(); ++i) {
    dims.push_back(shape.dim_size(i));
    nelems *= shape.dim_size(i);
  }
  CHECK_EQ(nelems, 0);
  return TF_NewTensor(
      dtype, reinterpret_cast<const int64_t*>(dims.data()), shape.dims(),
      reinterpret_cast<void*>(&empty), 0, [](void*, size_t, void*) {}, nullptr);