} else {
    // Convert I64 const array to I32.
    llvm::SmallVector<int32_t> const_i32_vec;
    for (auto element : const_value) {
      const_i32_vec.push_back(static_cast<int32_t>(element));
    }
    const_value_raw = rewriter.getI32TensorAttr(const_i32_vec);
  }
  Value result_const = builder.create<TF::ConstOp>(const_value_raw);
  return result_const;
}

      if (i != feature_index) {
        reduce_dim_axes.push_back(i);
      }
    }
    auto mean_dims = DenseIntElementsAttr::get(dims_type, reduce_dim_axes);
    ::mlir::TF::ConstOp reduce_dim_op =
        rewriter.create<TF::ConstOp>(bn_op.getLoc(), mean_dims);
    int64_t feature_dim_size = input_type.getDimSize(feature_index);
    auto mean_var_type = RankedTensorType::get(/*shape=*/{feature_dim_size},

}

TF::ConstOp Create1DConstantOp(const std::vector<int>& value, Location loc,
                               OpBuilder* builder) {
  auto type =
      mlir::RankedTensorType::get(value.size(), builder->getIntegerType(32));
  auto dense_values = mlir::DenseIntElementsAttr::get(type, value);
  return builder->create<TF::ConstOp>(loc, dense_values);
}

TF::ConstOp CreateScalarConstantOp(int value, Location loc,

  }

  const auto type = RankedTensorType::get(shape, builder.getF32Type());
  const auto value_attr = DenseFPElementsAttr::get(type, values);
  return builder.create<TF::ConstOp>(loc, value_attr);
}

// Creates a 1D array with integer/float type.
template <typename T>
Value Create1DConstValue(OpBuilder& builder, const Location loc,
                         const SmallVector<T>& values) {

    std::swap(rhs_shape[rhs_dims - 1], rhs_shape[rhs_dims - 2]);
  }

  const int64_t rows = lhs_shape[lhs_dims - 2];
  const int64_t cols = rhs_shape[rhs_dims - 1];

  if (lhs_shape[lhs_dims - 1] != rhs_shape[rhs_dims - 2]) {
    // Input dimensions must be compatible for multiplication.
    return failure();
  }

  const auto matmul_type = RankedTensorType::get({rows, cols}, element_type);

  // CHECK-NEXT: [[PREFIX:%.*]] = tf_mlrt.constop
  %cst = "tf.Const"() {__op_key = 0: i32, value = dense<"restore_ariables"> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>
  // CHECK-NEXT: [[SLICE:%.*]] = tf_mlrt.constop
  %cst_0 = "tf.Const"()  {__op_key = 1: i32, value = dense<""> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
  // CHECK-NEXT: [[NAME:%.*]] = tf_mlrt.constop

    IntegerAttr group_key = rewriter.getI32IntegerAttr(0);

    auto const_group_size = rewriter.create<TF::ConstOp>(
        assign_group->getLoc(), assign_group.getResult(0).getType(),
        group_size);
    auto const_group_key = rewriter.create<TF::ConstOp>(
        assign_group->getLoc(), assign_group.getResult(1).getType(), group_key);

    return rewriter.createOrFold<TF::CastOp>(loc, new_type, val,
                                             rewriter.getBoolAttr(false));
  }
  return rewriter.createOrFold<TF::CastOp>(
      loc, UnrankedTensorType::get(new_ele_type), val,
      rewriter.getBoolAttr(false));
}

// Utility function to-
// 1. Create a tfl.const op with an int32_t values, from an MLIR Value, if the

                                       OpBuilder builder, Location loc) {
  return builder.create<TF::ConstOp>(loc, tensor_elements);
}

// Returns ElementsAttr which has the value held by 'resource_tensor'.
ElementsAttr GetTensorValueAsElementsAttr(
    TF::VarHandleOp var_handle_op, const tensorflow::Tensor& resource_tensor,
    const tensorflow::DeviceMgr* mgr, OpBuilder builder) {
  if (resource_tensor.dtype() != tensorflow::DT_RESOURCE) {

namespace mlir {
namespace TF {

namespace {

// Creates ConstOp for int32_t value.
ConstOp createI32ConstOp(int32_t value, Location loc,
                         PatternRewriter* rewriter) {
  auto int_attr = IntegerAttr::get(rewriter->getIntegerType(32), value);
  return rewriter->create<ConstOp>(loc, int_attr);
}

// Creates ConstantOp for array of int32_t.