CreateXlaCallModuleOp(inputs, outputs, result_types, reverse_subgraph,
                        stablehlo_func_op, module_op);

  // 3) Erase the replaced ops.
  for (Operation* subgraph_op : reverse_subgraph) {
    subgraph_op->erase();
  }
}

// Contains the actual logic for updating states and replacing StableHLO ops
// with tf.XlaCallModuleOps.
void UpdateStatesAndReplaceStablehloOps(

    if (argnum_num_users[arg_num] != conv2d_and_block_sizes.size() ||
        conv2d_and_block_sizes.empty()) {
      argnum_and_convolutions.erase(arg_num);
      continue;
    }
    int64_t block_size = conv2d_and_block_sizes[0].second;
    if (block_size < 2) {
      argnum_and_convolutions.erase(arg_num);
      continue;
    }
    // Continue if not all the block sizes for space to depth transform are the
    // same.

        moduleA.moduleMetadata.expectGet()
        moduleA.artifact.expectGet()

        then:
        succeeds ":compileJava"
    }

    def 'constraint from platform does not erase excludes (platform: #platform)'() {
        given:
        platformModule("""
        constraints {
            api 'org:foo:1.0'
        }
""")

    }
    op_state.addTypes(output_tys);
    op_state.addAttributes(attr);
    auto stablehlo_op = builder.create(op_state);
    custom_op.replaceAllUsesWith(stablehlo_op);
    custom_op.erase();
  });
}

std::unique_ptr<OperationPass<func::FuncOp>> CreateTflToStablehloPass() {
  return std::make_unique<TflToStablehloPass>();
}

static PassRegistration<TflToStablehloPass> pass;

     * The compiler is happy with the casting that allows to hide the checked exception.
     * The runtime is happy with the casting because the checked exception type information is captured in a generic type parameter which gets erased.
     */
    private static <T, E extends Exception> Factory<T> toUncheckedThrowingFactory(final ThrowingFactory<T, E> throwingFactory) {
        return new Factory<T>() {
            @Nullable
            @Override

    op_property->inputs.erase(
        std::remove_if(
            op_property->inputs.begin(), op_property->inputs.end(),
            [&](std::pair<int, operator_property::TensorProperty> input) {
              return cifg_non_inputs.find(input.first) != cifg_non_inputs.end();
            }),
        op_property->inputs.end());
    op_property->intermediates.erase(

void PrepareQuantizeDRQPass::removeAllStatsOp(func::FuncOp func) {
  func.walk([&](quantfork::StatisticsOp stats_op) {
    stats_op.replaceAllUsesWith(stats_op.getArg());
    stats_op.erase();
  });
}

#include "tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_quantize.inc"

void PrepareQuantizeDRQPass::runOnOperation() {
  MLIRContext* ctx = &getContext();

		t.line = t.line[:t.pos]
		t.moveCursorToPos(t.pos)
	case keyCtrlD:
		// Erase the character under the current position.
		// The EOF case when the line is empty is handled in
		// readLine().
		if t.pos < len(t.line) {
			t.pos++
			t.eraseNPreviousChars(1)
		}
	case keyCtrlU:
		t.eraseNPreviousChars(t.pos)
	case keyClearScreen:
		// Erases the screen and moves the cursor to the home position.

  // structured lowering.
  old_parallel_execute.walk(
      [&](TF::_XlaCompileMlirPlaceholderProgramKeyOp key_op) {
        key_op.replaceAllUsesWith(compile_op->getResult(1));
        key_op.erase();
      });

  // After rewrite, if there is a TPUCompilationResultOp from the same cluster,
  // replace it with the result of the compile op. The TPUCompilationResultOp is

  }
}

// Strips off the tensor part of the tensor_name to get the node_name.
const string GetNodeNameFromTensorName(string tensor_name) {
  if (tensor_name[0] == '^') {
    tensor_name.erase(0, 1);
  }
  std::vector<string> tensor_name_parts = str_util::Split(tensor_name, ':');
  return tensor_name_parts[0];
}

// Gets the set of node names needed by `outputs` and the corresponding set of