}

// This corresponds to tensorflow::Tensor.
def TFTensorType : DialectType<Fallback_Dialect,
    CPred<"$_self.isa<::tfrt::fallback::TFTensorType>()">, "!tfrt_fallback.tf_tensor type">,
    BuildableType<"$_builder.getType<::tfrt::fallback::TFTensorType>()"> {
  let description = [{
    `!tfrt_fallback.tf_tensor type` represents a tensorflow::Tensor.
  }];
}

// This corresponds to tensorflow::Allocator.

};

// Lowers a general tensor argument that is used as a condition to a functional
// control flow op into an i1 value.
static Value LowerCondition(Location loc, Value value, OpBuilder* builder) {
  Value zero_d = builder->create<ToBoolOp>(loc, value);
  Value scalar = builder->create<tensor::ExtractOp>(loc, zero_d);
  return scalar;
}

// Calls the function `fn` with arguments provided by the given function and

                                       OpBuilder& builder) {
  const auto filename_op_type = RankedTensorType::get(
      /*shape=*/{}, /*elementType=*/builder.getType<TF::StringType>());
  const auto file_prefix_attr = builder.getStringAttr(kTfFilePrefix);
  const auto arg_attrs = builder.getDictionaryAttr({builder.getNamedAttr(
      kTfSavedModelIndexPathAttr, builder.getArrayAttr({file_prefix_attr}))});

    auto module = getOperation();

    mlir::OpBuilder op_builder(&module.getBodyRegion());
    // Cannot use the pattern rewriter because the driver applies folders by
    // default.
    module.walk([&](mhlo::ConstantOp const_op) {
      UnfoldSplatConstant(&op_builder, const_op);
    });
  }

 private:
  void UnfoldSplatConstant(mlir::OpBuilder* op_builder,
                           mhlo::ConstantOp const_op) const {

  void runOnOperation() override {
    auto module = getOperation();

    mlir::OpBuilder op_builder(&module.getBodyRegion());
    module.walk([&](mlir::arith::ConstantOp const_op) {
      MaybeUnfoldLargeSplatConstant(&op_builder, const_op);
    });
  }

 private:
  void MaybeUnfoldLargeSplatConstant(mlir::OpBuilder* op_builder,
                                     mlir::arith::ConstantOp const_op) const {

                          GetIndicesForElement(index, buffer, builder, loc)})
      .getOutput();
}

TensorType GetSizeType(OpBuilder builder) {
  return tensorflow::GetTypeFromTFTensorShape({1}, builder.getIntegerType(32));
}

Value ReshapeScalarToSizeType(OpBuilder builder, Value scalar, Location loc) {
  auto size_type = GetSizeType(builder);
  return builder.create<TF::ReshapeOp>(
      loc, ArrayRef<Type>{size_type},

  auto reducer =
      builder->create<Op>(loc, block->getArgument(0), block->getArgument(1));
  builder->create<ReturnOp>(loc, reducer.getResult());
}

ConstantOp GetScalarConstOfType(Type ty, Location loc, int64_t raw_value,
                                OpBuilder* builder);

ConstantOp GetScalarNegZeroOfType(Type ty, Location loc, OpBuilder* builder);

def IsPrecisionEmpty :
  Constraint<CPred<"IsPrecisionEmpty($0)">>;

// Creates Einsum Op from XlaDotV2 Op by generating equation.
def CreateEinsumOpFromXlaDotV2Op : NativeCodeCall<
  "CreateEinsumOpFromXlaDotV2Op($_builder, $_loc, $0...)">;

// Convert XlaDotV2 Op to Einsum Op with above two functions.
def ConvertXlaDotV2OpToEinsumOp : Pat<
  (TF_XlaDotV2Op:$dot $lhs, $rhs, $dot_dimension_numbers, $precision_config),

    return read.emitOpError("unknown tensor array");
  }
  OpBuilder builder(read);
  auto buffer = cutil::ReadLocalVariable(local_var, builder, read.getLoc());
  auto index_reshape =
      cutil::ReshapeScalarToSizeType(builder, read.getIndex(), read.getLoc());
  auto elem = cutil::GetElement(index_reshape, buffer, builder, read.getLoc());
  ReplaceAllUsesExceptTerminator(read.getValue(), elem);

      tensorflow::DefaultRngAlgForDeviceType(device_type_);
  tensorflow::Algorithm tensorflow_rng =
      tensorflow::ToTensorflowAlgorithm(xla_rng);

  OpBuilder opbuilder(get_alg_op);

  auto tf_const = opbuilder.create<TF::ConstOp>(
      get_alg_op->getLoc(), opbuilder.getI32IntegerAttr((int)tensorflow_rng));

  get_alg_op->replaceAllUsesWith(tf_const);
  get_alg_op->erase();
  return success();
}