auto reshape_sizes = DenseIntElementsAttr::get(reshape_type, new_shape);
  auto reshape_value =
      builder->create<TF::ConstOp>(input.getLoc(), reshape_sizes);
  return builder->create<TF::ReshapeOp>(input.getLoc(), reshape_result_type,
                                        input, reshape_value);
}

// Creates transpose op for shape to depth transform.

        if (IsCompatibleTypeWithTFLCastOp(value.getType()) &&
            IsCompatibleTypeWithTFLCastOp(type)) {
          auto cast = b.create<CastOp>(yield_op->getLoc(), type, value);
          args.push_back(cast);
        } else {
          auto cast = b.create<TF::CastOp>(yield_op->getLoc(), type, value);
          args.push_back(cast);
        }
      }
    }
    args.append(new_args.begin(), new_args.end());
  } else {

        mlir::cast<TensorType>(input.getType()), kNchwToNhwcPermutation);

    auto input_transpose_op = rewriter.create<mlir::stablehlo::TransposeOp>(
        op.getLoc(), /*resultType0=*/new_input_tensor_type, /*operand=*/input,
        rewriter.getDenseI64ArrayAttr(kNchwToNhwcPermutation));

    // Transpose the filter tensor: [o, i, 0, 1] => [0, 1, i, o]
    Value filter = op->getOperand(1);

  }

  auto compile_op = builder->create<TF::_TPUCompileMlirOp>(
      cluster_func.getLoc(),
      /*compilation_status=*/compilation_status_type, /*program=*/
      llvm::SmallVector<Type, 8>(num_cores_per_replica, program_type),
      compile_op_operands, txt_module, txt_metadata);

  return tensorflow::WrapOpInLaunch(builder, compile_op.getLoc(), compile_op,
                                    compilation_device);
}

  // Unformat op.
  auto unformat_op = builder.create<TF::TPUReshardVariablesOp>(
      while_op.getLoc(), llvm::ArrayRef<Type>{}, unformat_operands);
  WrapOpInLaunch(&builder, execute_launch.getLoc(), unformat_op,
                 execute_launch.getDevice());
  builder.create<tf_device::ReturnOp>(while_op.getLoc(), ArrayRef<Value>{});

  return true;
}

void TPUVariableRuntimeReformattingPass::runOnOperation() {

      block_arg.getLoc(),
      DenseIntElementsAttr::get(
          RankedTensorType::get({1, num_replicas}, builder.getIntegerType(32)),
          group_assignment_val));

  StringAttr reduce_op = builder.getStringAttr("Add");
  StringAttr mode = builder.getStringAttr("CrossReplica");
  return builder.create<XlaAllReduceOp>(block_arg.getLoc(), block_arg.getType(),

    auto *new_ewise_op =
        rewriter.create(op->getLoc(), op->getName().getIdentifier(),
                        {tpose_arg1.getOperand(0), tpose_arg2.getOperand(0)},
                        new_out_type, op->getAttrs());

    // Apply original tranpose to output of ewise op.
    auto out_tpose_op = rewriter.create<TFL::TransposeOp>(
        new_ewise_op->getLoc(), op->getResult(0).getType(),

  OpBuilder builder(func_.getBody());
  auto op = builder.create<mlir::TFL::NonMaxSuppressionV4Op>(
      func_.getLoc(), output_type0, output_type1, boxes, scores,
      max_output_size, iou_threshold, score_threshold);

  builder.create<mlir::func::ReturnOp>(func_.getLoc(), op.getResults());
}

LogicalResult ConvertNMSPaddedFunc::VerifySignature() {
  // Verify high-level function signature.

            const_op->getLoc(),
            DenseElementsAttr::get(
                tensorflow::GetTypeFromTFTensorShape(
                    {}, splat_elements_attr.getType().getElementType()),
                splat_elements_attr.getSplatValue<Attribute>()));
    TFL::FillOp fill = op_builder->create<TFL::FillOp>(
        const_op->getLoc(), splat_elements_attr.getType(), fill_shape,
        fill_value);

        CreateI8F32UniformQuantizedType(
            uniform_quantize_call_op.getLoc(), *rewriter.getContext(),
            input_scale_value, input_zero_point_value);
    auto input_uniform_quantize_op =
        rewriter.create<stablehlo::UniformQuantizeOp>(
            uniform_quantize_call_op.getLoc(),
            /*result=*/
            mlir::cast<TensorType>(input_value.getType())