tf_gen_op_libs(
    op_lib_names = ["bitcast"],
    deps = [
        "//tensorflow/c:ops",
        "//tensorflow/c:tf_datatype",
        "//tensorflow/c:tf_status",
        "//tensorflow/c:tf_tensor",
        "//tensorflow/core:lib",
    ],
)

tf_gen_op_libs(
    op_lib_names = ["summary"],
    deps = [
        "//tensorflow/c:ops",
        "//tensorflow/c:tf_status",
        "//tensorflow/core:lib",
    ],
)

#include "mlir/IR/TypeUtilities.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Transforms/InliningUtils.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tfrt/ir/mlrt/mlrt_dialect.h"

namespace tensorflow {
namespace tf_mlrt {

namespace {

struct TensorflowMlrtInlinerInterface : public mlir::DialectInlinerInterface {

  @parameterized.parameters(
      testing.parameter_combinations([{
          'bias_fn': (
              None,
              nn_ops.bias_add,
          ),
          'activation_fn': (
              None,
              nn_ops.relu,
              nn_ops.relu6,
          ),
          'dim_sizes': (
              # tf.MatMul cases.
              ([None, 1024], [1024, 3]),  # dynamic batch dim.

    const Tensor& cc_b, Tensor* cc_out,
    void (*binary_add_func)(TF_OpKernelContext* ctx, TF_Tensor* a, TF_Tensor* b,
                            TF_Tensor* out)) {
  if (cc_a.dtype() == ::tensorflow::DT_INVALID) {
    *cc_out = cc_b;
    return absl::OkStatus();
  }
  if (cc_b.dtype() == ::tensorflow::DT_INVALID) {
    *cc_out = cc_a;
    return absl::OkStatus();
  }

  Status status;

include "mlir/IR/OpBase.td"
include "mlir/Dialect/Func/IR/FuncOps.td"
include "mlir/Dialect/Arith/IR/ArithOps.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td"
include "stablehlo/dialect/StablehloOps.td"
include "tensorflow/compiler/mlir/quantization/common/attrs_and_constraints.td"

      // $orig_input_shape
      CompileTimeConstantOperand<TF::AvgPoolGradOp, 0>,
      // $block_shape, $crops
      CompileTimeConstantOperand<TF::BatchToSpaceNDOp, 1, 2>,
      CompileTimeConstantOperand<TF::BatchToSpaceOp, 1>,      // $crops
      CompileTimeConstantOperand<TF::BincountOp, 1>,          // $size
      CompileTimeConstantOperand<TF::BroadcastArgsOp, 0, 1>,  // $s0, $s1

)

exports_files([
    "ir/tf_generated_ops.td",
    "ir/tf_op_base.td",
    "ir/tf_op_interfaces.td",
    "ir/tf_ops.td",
])

td_library(
    name = "tensorflow_ops_td_files",
    srcs = [
        "ir/tf_generated_ops.td",
        "ir/tf_op_base.td",
        "ir/tf_op_interfaces.td",
        "ir/tf_ops.td",
    ],
    compatible_with = get_compatible_with_portable(),
    deps = [

        mean, variance = scale, offset
        out = nn_ops.conv2d(
            input_tensor,
            self.filters,
            strides=strides,
            dilations=dilations,
            padding=padding,
            data_format='NHWC',
            name='sample/conv',
        )
        if bias_fn is not None:
          out = nn_ops.bias_add(out, self.bias)
        if has_batch_norm:

include "mlir/Dialect/Func/IR/FuncOps.td"
include "mlir/Dialect/Arith/IR/ArithOps.td"
include "tensorflow/compiler/mlir/quantization/common/attrs_and_constraints.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td"

// Converts tf.Const to arith.constant for statically shaped, non-opaque constants.
// Needed for QuantizationDriver to recognize constants.
def ConvertTfConstToArithConst : Pat<

from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_spec
from tensorflow.python.module import module
from tensorflow.python.ops import io_ops
from tensorflow.python.ops import lookup_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.saved_model import save_options