auto tf_reduce_op = rewriter.create<BooleanReduce>(
          reduce_op.getLoc(), reduce_op->getResult(0).getType(), operand,
          reduction_indices,
          /*keep_dim=*/rewriter.getBoolAttr(false));
      auto tf_argreduce_op = rewriter.create<ArgReduce>(
          reduce_op.getLoc(), reduce_op->getResult(1).getType(), operand,
          reduction_indices);

      OpBuilder builder(op);
      Operation* load = builder.create<mlir::ml_program::GlobalLoadOp>(
          op.getLoc(), globalOp.getType(),
          SymbolRefAttr::get(op->getContext(), globalOp.getSymName()));
      if (globalOp.getType() != op.getValue().getType()) {
        load = builder.create<TF::CastOp>(op.getLoc(), op.getValue().getType(),
                                          load->getResult(0));
      }

  builder.insert(var_handle_op_in_init);
  auto const_op = builder.create<mlir::arith::ConstantOp>(
      session_init_func.getLoc(), tensor_attr.getType(), tensor_attr);

  builder.create<TF::AssignVariableOp>(
      session_init_func.getLoc(), llvm::ArrayRef<mlir::Type>{},
      llvm::ArrayRef<mlir::Value>{var_handle_op_in_init->getResult(0),
                                  const_op.getResult()});
}

    Type output_type = bmm_op.getResult().getType();
    auto no_input = rewriter.create<TFL::NoValueOp>(
        bmm_op->getLoc(), rewriter.getNoneType(), rewriter.getUnitAttr());
    auto fc_op = rewriter.create<TFL::FullyConnectedOp>(
        bmm_op->getLoc(), ArrayRef<Type>{output_type},
        /*input=*/output_lhs, /*filter=*/output_rhs, /*bias=*/no_input,

      replacer.create<mlir::func::ReturnOp>(yield_op.getLoc(),
                                            yield_op.getOperands());
      yield_op.erase();
    }

    // Remap the captured operands in the (former) island block with newly
    // created entry block arguments in the function body.
    {
      Block &entry_block = outlined_func.getBody().front();
      auto loc = outlined_func.getLoc();
      for (Value operand : operands) {

        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);

 * It features an <code>execute()</code> method, which triggers the Mojo's build-process behavior, and can throw
 * a MojoExecutionException or MojoFailureException if error conditions occur.<br>
 * Also included is the <code>setLog(...)</code> method, which simply allows Maven to inject a logging mechanism which
 * will allow the Mojo to communicate to the outside world through standard Maven channels.
 *
 */
public interface Mojo {

    }
    Value broadcast_multiplier = rewriter.create<mhlo::BroadcastInDimOp>(
        mul_op.getLoc(), filter.getType(), multiplier, broadcast_dims);
    Value new_filter = rewriter.create<mhlo::MulOp>(
        mul_op.getLoc(), filter.getType(), filter, broadcast_multiplier);
    Value new_conv = rewriter.create<mhlo::ConvolutionOp>(
        mul_op.getLoc(), conv_op.getType(), conv_op.getLhs(), new_filter,

  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);