// CHECK: return %[[identity]] : tensor<1x1x2xf32>

// Tests that an AddV2 op accepting two bf16 operands is transformed into
// an AddV2 op that accepts two fp32 operands.
func.func @cast_bf16_add_v2_to_fp32(%arg0: tensor<2xbf16>, %arg1: tensor<2xbf16>) -> tensor<2xf32> {
  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<2xbf16>, tensor<2xbf16>) -> tensor<2xbf16>

    $operands_with_static_shape are indices of operands that are tagged with a maximum static shape.
    $producer_name is a string describing the name of the framework that added support for running this portion of the model on TPUs.

    Example:
      %rendezvous_key_base, %result = tf_mlrt_tpu.compile_and_execute (%operands) constant_operand_indices = [1, 3] metadata = "metadata..." mlir_module = "mlir_module..."

    $operands_with_static_shape are indices of operands that are tagged with a maximum static shape.
    $producer_name is a string describing the name of the framework that added support for running this portion of the model on TPUs.

    Example:
      %rendezvous_key_base, %result = tf_mlrt_tpu.compile_and_execute (%operands) constant_operand_indices = [1, 3] metadata = "metadata..." mlir_module = "mlir_module..."

    // Forward island fetches (tf_executor.yield operands) to island op result
    // uses.
    for (auto result :
         llvm::zip(island_op.getOutputs(), island_op.GetYield().getFetches()))
      std::get<0>(result).replaceAllUsesWith(std::get<1>(result));
  }

  // Forward graph fetches (tf_executor.fetch operands) to graph op result uses.
  for (auto result :

    constant_fold_hook_ = std::move(fn);
  }

  static LogicalResult constantFold(Operation *op, ArrayRef<Attribute> operands,
                                    SmallVectorImpl<OpFoldResult> &results) {
    if (constant_fold_hook_) return constant_fold_hook_(op, operands, results);
    return failure();
  }

  static bool HasConstantFoldHook() { return constant_fold_hook_; }

// license that can be found in the LICENSE file.

package p

type resultFlags uint

// Example from #52031.
//
// The following shifts should not produce errors on Go < 1.13, as their
// untyped constant operands are representable by type uint.
const (
	_ resultFlags = (1 << iota) / 2

	reportEqual
	reportUnequal
	reportByIgnore
	reportByMethod
	reportByFunc
	reportByCycle
)

// Invalid cases.

namespace TF {

// Checks whether the given TF operation can be folded or not.
bool CanBeFolded(Operation* inst);

// Evaluates the operation with given operand values.
LogicalResult EvaluateOperation(Operation* inst,
                                llvm::ArrayRef<ElementsAttr> operands,
                                llvm::SmallVector<Attribute>& results);

}  // namespace TF
}  // namespace mlir

// -> tfl.batch_matmul(mhlo.transpose(mhlo.reshape(operand)), ...).
// Note:
// 1) Reshape/transpose are inserted because tfl.BatchMatMul requires
// size(contracting_dimensions) = 1 and size(output_dim) = 1, whereas
// mhlo.dot_general has no such restriction.
// 2) Inserted mhlo.reshape/transpose will be legalized to tf.reshape/transpose
// in LegalizeHloToTf (then from tf to tfl later).
// 3) If the operands are dynamic shaped tensors, mhlo.DynamicReshapeOp is

// commutativity of the AddV2: we want the FusedBatchNormV3 on any side.
// Also we need some native calls to handle the "hasOneUse" aspects and the
// optional extra operands for the AddV2 case.
struct ReluToFusedBatchNorm : public OpRewritePattern<ReluOp> {
  using OpRewritePattern<ReluOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(ReluOp relu_op,

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir {
namespace quant {

// Copied from tensorflow/compiler/mlir/lite/transforms/prepare_tf.cc.
// By removing identity ops, constant operands with dynamic shapes have static
// shape information which is necessary for correct pattern matching in this
// pass.
struct RemoveIdentity : public OpRewritePattern<TF::IdentityOp> {