const Graph& graph = session->graph->graph;

    const string& mutation_warning = session->graph->sessions[session];
    if (!mutation_warning.empty()) {
      // TODO(b/74949947): turn this back into an error status
      LOG(WARNING) << mutation_warning;
      session->graph->sessions[session].clear();
    }

    const auto num_nodes = graph.num_node_ids();

include "mlir/Pass/PassBase.td"

// TF dialect passes.

def TensorflowGPUFusion : Pass<"tf-gpu-op-fusion", "mlir::func::FuncOp"> {
  let summary = "Fusion optimization for GPU targets";
  let description = [{
    This pass is performing fusion specific to GPU targets. This is an ad-hoc
    pass for now, but should be integrated with some notion of "target" in the
    MLIR pipeline in the future.

        list(original_signature_map.keys()), set(signatures.keys())
    )
    self.assertDictEqual(original_signature_map, converted_signature_map)

  def test_duplicated_tensor_name(self):
    with session.Session(graph=ops.Graph()) as sess:
      input_tensor = array_ops.placeholder(
          dtypes.float32, shape=[], name='input'
      )
      q_input = array_ops.fake_quant_with_min_max_args(

        /* Downgrade one transport to the lower of the negotiated buffer sizes
         * so we can just send whatever is received.
         */

        SmbTransport t1 = tree.session.transport;
        SmbTransport t2 = dest.tree.session.transport;

        if( t1.snd_buf_size < t2.snd_buf_size ) {
            t2.snd_buf_size = t1.snd_buf_size;
        } else {
            t1.snd_buf_size = t2.snd_buf_size;

  if (GetReshapeOutputType(input, shape, error_handler, output_type)
          .succeeded()) {
    inferredReturnTypes.assign({output_type});
    return success();
  }
  Type result_type;
  result_type = UnrankedTensorType::get(
      input.getType().cast<ShapedType>().getElementType());
  inferredReturnTypes.assign({result_type});
  return success();
}

		ldr.SetSymAlign(ldr.Lookup("runtime.bss", 0), state.dataMaxAlign[sym.SBSS])
	}

	// Create *sym.Section objects and assign symbols to sections for
	// data/rodata (and related) symbols.
	state.allocateDataSections(ctxt)

	state.allocateSEHSections(ctxt)

	// Create *sym.Section objects and assign symbols to sections for
	// DWARF symbols.
	state.allocateDwarfSections(ctxt)

	/* number the sections */

	"𝔸":                            "\U0001d538",
	"⁡":                   "\u2061",
	"Å":                           "\u00c5",
	"𝒜":                            "\U0001d49c",
	"≔":                          "\u2254",
	"Ã":                          "\u00c3",
	"Ä":                            "\u00c4",
	"∖":                       "\u2216",
	"⫧":                            "\u2ae7",

// Result, Rows)
type driverConn struct {
	db        *DB
	createdAt time.Time

	sync.Mutex  // guards following
	ci          driver.Conn
	needReset   bool // The connection session should be reset before use if true.
	closed      bool
	finalClosed bool // ci.Close has been called
	openStmt    map[*driverStmt]bool

	// guarded by db.mu
	inUse      bool

		"Aopf;":                            '\U0001D538',
		"ApplyFunction;":                   '\U00002061',
		"Aring;":                           '\U000000C5',
		"Ascr;":                            '\U0001D49C',
		"Assign;":                          '\U00002254',
		"Atilde;":                          '\U000000C3',
		"Auml;":                            '\U000000C4',
		"Backslash;":                       '\U00002216',

        } else {
            // TODO - the suffix should be a deterministic function of the known and enabled annotations
            // For now, just assign using a counter
            suffix = ClassGeneratorSuffixRegistry.assign("$Decorated");
            generatedClasses = cacheFactory.newClassMap();
        }