* are valid.
   *
   * @param rowKey row key of mapping to be erased
   * @param columnKey column key of mapping to be erased
   * @return the value previously associated with the keys, or {@code null} if no mapping existed
   *     for the keys
   */
  @CanIgnoreReturnValue
  @CheckForNull
  public V erase(@CheckForNull Object rowKey, @CheckForNull Object columnKey) {

    new_outputs.push_back(it->getSecond().size);
  }
  OpBuilder(old_terminator)
      .create<TerminatorOp>(old_terminator->getLoc(), new_outputs);
  old_terminator->erase();
  return output_buffer_to_size;
}

// Adds the corresponding sizes of tensor list buffers in func's return values
// to the list of return values. Returns the mapping from the buffer indices to

  builder.create<mlir::tf_device::ReturnOp>(result_op->getLoc(), results);

  // Then erase all the identity and partitioned output ops.
  for (const auto& [_, ops] : partitioned_outputs) {
    for (mlir::TF::TPUPartitionedOutputV2Op op : ops) {
      op->erase();
    }
  }

  for (mlir::TF::IdentityOp to_erase : erase_list) {
    to_erase->erase();
  }

  return result_op;
}

            return value;
        }
    }

    /**
     * ED (Erase in Display) / EL (Erase in Line) parameter (see
     * <a href="https://en.wikipedia.org/wiki/ANSI_escape_code#CSI_sequences">CSI sequence J and K</a>)
     * @see Ansi#eraseScreen(Erase)
     * @see Ansi#eraseLine(Erase)
     */
    public enum Erase {
        FORWARD(0, "FORWARD"),
        BACKWARD(1, "BACKWARD"),
        ALL(2, "ALL");

      if (failed(HandleStackPopV2Op(pop, data_var_to_size_var))) {
        return failure();
      }
    } else if (auto close = llvm::dyn_cast<TF::StackCloseV2Op>(&op)) {
      data_var_to_size_var->erase(close.getHandle());
      close.erase();
    } else if (auto while_op = llvm::dyn_cast<TF::WhileOp>(&op)) {
      if (failed(HandleWhileOp(while_op, module, *data_var_to_size_var,

  scalar_tensor.scalar<float>()() = 0.0f;
  auto flow = builder.create<TF::ConstOp>(
      ta.getLoc(), tensorflow::ConvertTensor(scalar_tensor, &builder).value());
  ta.getFlow().replaceAllUsesWith(flow);
  ta.erase();
  (*stats)[local_var].accumulate_on_write = false;
  return success();
}

LogicalResult HandleTensorArrayReadV3Op(
    TF::TensorArrayReadV3Op read,

    // Updates usage and erases rewritten reduce_dataset op based on the number
    // of state variables.
    for (int i = 0; i < state_size; ++i) {
      reduce_dataset.getResult(i).replaceAllUsesWith(
          dataset_while.getResult(i + 1));
    }
    reduce_dataset.erase();

    return WalkResult::advance();
  });

  }

  // Relocate the main function's FetchOp to the last.
  main_body.push_back(main_fetch_op->clone(mapper));
  main_fetch_op.erase();

  auto cloned_fetch_op = cast<FetchOp>(src_graph_op.GetFetch()->clone(mapper));
  Value control_fetch = *cloned_fetch_op.getFetches().begin();
  cloned_fetch_op.erase();

  return control_fetch;
}

// Creates a new `IdentityOp` wrapped by an `IslandOp`. The identity op returns

  // reinserted into the cache in UpdateLRU.
  entry->second->timestamp = 0;
  lru_list_.erase(entry->second->lru_iterator);
  lra_list_.erase(entry->second->lra_iterator);
  cache_size_ -= entry->second->data.capacity();
  block_map_.erase(entry);
}

             /*caller_region=*/if_region.getElseBranch(), if_op.getInput(),
             /*use_region_args=*/false,
             /*forward_block_args=*/false);
  if_op.replaceAllUsesWith(if_region.getResults());
  if_op.erase();
  return success();
}

LogicalResult ConvertCaseOp(CaseOp case_op) {
  OpBuilder builder(case_op);
  auto case_region = builder.create<TF::CaseRegionOp>(