return {};
  }

  int64_t num_elements = value_shape.getNumElements();
  SmallVector<int64_t> new_shape;
  for (auto idx : llvm::reverse(llvm::seq<int32_t>(0, rhs_shape.getRank()))) {
    const int64_t rhs_dim = rhs_shape.getDimSize(idx);
    if (num_elements % rhs_dim != 0) {
      return {};
    }
    new_shape.push_back(rhs_dim);
    num_elements = num_elements / rhs_dim;
    if (num_elements == 1) break;
  }

    operand_new_shape[dimensions[i]] = operand.getType().getDimSize(i);
  }

  llvm::SmallVector<ElementValueT, 16> new_values;
  auto num_elements = result_type.getNumElements();
  new_values.reserve(num_elements);
  auto operand_values = operand.getValues<ElementValueT>();
  for (int64_t i = 0; i < num_elements; ++i) {
    const int64_t operand_index =
        GetElementIndex(operand_new_shape, current_index);

    int num_elements_threshold = quant_options_.min_num_elements_for_weights();
    int num_elements = cast<ShapedType>(op.getType()).getNumElements();
    if (num_elements < num_elements_threshold) {
      op->emitRemark("Quantization is skipped because the op has ")
          << num_elements << " elements which is fewer than the threshold("
          << num_elements_threshold << " elements).";
      return failure();

    const int64_t x_size =
        x_shapes[x_idx].num_elements() * JacobianStride<X_T>::value;
    for (int y_idx = 0; y_idx < y_num; y_idx++) {
      // The number of columns is the number of elements in the y tensor
      // multiplied by the number of Jacobian entries needed to represent each
      // y type.
      const int64_t y_size =
          y_shapes[y_idx].num_elements() * JacobianStride<Y_T>::value;

      b_size->push_back(block_size[i]);
    }
  }
}

inline float GetSparsity(const int num_zeros, const int num_elements) {
  return (1.0 * num_zeros / num_elements);
}

float CalculateRandomSparsity(const ElementsAttr& attr,
                              const ShapedType& type) {
  int num_elements = type.getNumElements();
  int num_zeros = 0;

  if (mlir::isa<FloatType>(type.getElementType())) {

  return th;
}

TFE_TensorHandle* TestMatrixTensorHandle100x100(TFE_Context* ctx) {
  constexpr int64_t dims[] = {100, 100};
  constexpr int num_elements = dims[0] * dims[1];
  float data[num_elements];
  for (int i = 0; i < num_elements; ++i) {
    data[i] = 1.0f;
  }
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, TF_FLOAT, &dims[0],

      // This op is guaranteed to be a constant as ODS checks IsConstTensor.
      // Check if the number of elements meets the requirement.
      int64_t num_elements =
          mlir::cast<ShapedType>(call_op.getOperand(0).getType())
              .getNumElements();
      if (num_elements < quant_options_.min_num_elements_for_weights()) {
        return absl::InternalError(
            "The params of Gather have fewer number of elements than "

      // zero reserved elements
      %num_elements = "tf.Const"() <{value = dense<0> : tensor<i32>}> {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : () -> tensor<i32>

      %list = "tf.TensorListReserve"(%elem_shape, %num_elements) : (tensor<i32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<64x1xbf16>>>

      const PartialTensorShape node_shape(node_value.tensor().tensor_shape());
      if (node_shape.num_elements() < 0) {
        return absl::FailedPreconditionError(absl::StrCat(
            "Saved model contains node \"", node.name(), "\" (op \"", node.op(),
            "\") which initializes from a tensor with ",
            node_shape.num_elements(), " elements"));
      }
    }
  } else if (node.op() == "Const") {

  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return -1;
  }

  int64_t num_elements = -1;
  status->status = tensorflow::unwrap(h)->NumElements(&num_elements);
  return num_elements;
}

int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index,
                            TF_Status* status) {
  if (h == nullptr) {