} else {
      initial_value = mlir::Attribute();
    }
    opToName[globalTensor] = name;
    auto variableOp = globalBuilder.create<ml_program::GlobalOp>(
        globalTensor.getLoc(), name, globalTensor.getType(),
        globalTensor.getIsMutable(), initial_value,
        /*visibility=*/globalBuilder.getStringAttr("private"));
    variableOp.setPrivate();
  }

  SymbolTable syms(module);

  SendTPUEmbeddingGradientsOp send_op;
  if (failed(GetOp(region, &send_op))) return failure();

  // No TPU embedding ops.
  if (!recv_op && !send_op) return success();

  Location loc = recv_op ? recv_op.getLoc() : send_op.getLoc();
  StringRef config = recv_op ? recv_op.getConfig() : send_op.getConfig();

  // Create XlaRecvTPUEmbeddingDeduplicationData op.
  OpBuilder builder(region);
  auto output_ty =

  void runOnOperation() override;
};

void ReplaceClusterReturnWithReturn(tf_device::ReturnOp cluster_return_op,
                                    OpBuilder* builder) {
  builder->create<func::ReturnOp>(cluster_return_op.getLoc(),
                                  cluster_return_op.getOperands());
  cluster_return_op.erase();
}

// Builds a function that outlines region attached to cluster_op or launch_op,

  SmallVector<Value, 4> tuple_operands;
  for (auto idx : llvm::seq<int32_t>(0, end))
    tuple_operands.push_back(
        builder.create<GetTupleElementOp>(value.getLoc(), value, idx)
            .getResult());

  return CreateTuple(builder, value.getLoc(), tuple_operands);
}

// Replaces uses of `values` with `replacements`. If `flatten_tuple` is false,

    // TODO: Map to a qbarrier with an attribute like [Forced] to signal that
    // this is a forced/hard-coded constraint.
    auto qbarrier = rewriter.create<QuantizeCastOp>(op.getLoc(), quantizedType,
                                                    op.getInputs());
    rewriter.replaceOpWithNewOp<DequantizeCastOp>(op, converter.input_type,

    auto pi = builder.create<TF::TPUPartitionedInputV2Op>(
        op.getLoc(), op.getType(), op.getOperands(),
        builder.getI64ArrayAttr(partition_dims), builder.getBoolAttr(false),
        op.get_XlaShardingAttr());
    op->replaceAllUsesWith(pi);
  } else {
    auto po = builder.create<TF::TPUPartitionedOutputV2Op>(
        op.getLoc(), op.getResultTypes(), op.getOperand(),

    FilterOutBlockArgControlDep(op->getOperands().drop_front(), new_operands);

    auto source_op = builder.create<tf_executor::NextIterationSourceOp>(
        op->getLoc(), op->getOperand(0).getType());
    builder.create<tf_executor::NextIterationSinkOp>(
        op->getLoc(), source_op.getToken(), /*input=*/op->getOperand(0),
        /*controlInputs=*/new_operands);
    op->replaceAllUsesWith(

    return failure();
  }
  auto op = builder.create<CustomOp>(
      func_.getLoc(), func_.getFunctionType().getResults(),
      func_.getArguments(), kMaxUnpooling,
      CustomOption(&builder, custom_option_buffer));
  builder.create<func::ReturnOp>(func_.getLoc(), op.getResults());

  return success();
}

LogicalResult ConvertMaxUnpoolingFunc::VerifySignature() {

  OpBuilder builder(&module->getBodyRegion());
  context.loadDialect<TF::TensorFlowDialect>();

  Value value = CreateConstValue<int8_t>(builder, module->getLoc(),
                                         unpacked_shape, unpacked_values);
  Value packed_value = PackOperand(builder, module->getLoc(), value, pack_dim);
  DenseIntElementsAttr packed_value_attr;
  ASSERT_TRUE(matchPattern(packed_value, m_Constant(&packed_value_attr)));

        maxs.push_back(rmax);
      }
      quant_type = quantfork::fakeQuantAttrsToType(
          op.getLoc(), num_bits, *op.getAxis(), mins, maxs, narrow_range,
          expressed, is_signed);
      if (legacy_float_scale) {
        quant_type = DownCastScale(quant_type, mins, maxs, op->getLoc());
      }
    } else if (auto stats =
                   op.getLayerStats().dyn_cast<DenseFPElementsAttr>()) {