stmt.DB.Dialector.BindVarTo(writer, stmt, v)
			}
		}
	}
}

// AddClause add clause
func (stmt *Statement) AddClause(v clause.Interface) {
	if optimizer, ok := v.(StatementModifier); ok {
		optimizer.ModifyStatement(stmt)
	} else {
		name := v.Name()
		c := stmt.Clauses[name]
		c.Name = name
		v.MergeClause(&c)
		stmt.Clauses[name] = c
	}
}

    2.11.
*   `tf.keras.optimizers.experimental.Optimizer` will graduate in Release 2.11,
    which means `tf.keras.optimizers.Optimizer` will be an alias of
    `tf.keras.optimizers.experimental.Optimizer`. The current
    `tf.keras.optimizers.Optimizer` will continue to be supported as
    `tf.keras.optimizers.legacy.Optimizer`,
    e.g.,`tf.keras.optimizers.legacy.Adam`. Most users won't be affected by this

TF_CAPI_EXPORT unsigned char TF_GetXlaConstantFoldingDisabled();
TF_CAPI_EXPORT void TF_SetXlaConstantFoldingDisabled(
    unsigned char should_enable);

// Create a serialized tensorflow.ConfigProto proto, where:
//
// a) ConfigProto.optimizer_options.global_jit_level is set to ON_1 if
// `enable_xla_compilation` is non-zero, and OFF otherwise.
// b) ConfigProto.gpu_options.allow_growth is set to `gpu_memory_allow_growth`.

//tensorflow/core:image_testdata
//tensorflow/core/kernels/cloud:bigquery_reader_ops
//tensorflow/python:extra_py_tests_deps
//tensorflow/python:mixed_precision
//tensorflow/python:tf_optimizer
//tensorflow/python:compare_test_proto_py
//tensorflow/python/framework:test_file_system.so
//tensorflow/python/debug:grpc_tensorflow_server.par
//tensorflow/python/feature_column:vocabulary_testdata

//tensorflow/core/lib/lmdb/testdata:lmdb_testdata
//tensorflow/core/kernels/cloud:bigquery_reader_ops
//tensorflow/python:extra_py_tests_deps
//tensorflow/python:mixed_precision
//tensorflow/python:tf_optimizer
//tensorflow/python:compare_test_proto_py
//tensorflow/python/framework:test_ops_2
//tensorflow/python/framework:test_file_system.so
//tensorflow/python/debug:grpc_tensorflow_server.par

   *
   * The alternative natural refactorings into void or Exception-returning methods are much slower.
   * This is a big deal - we're talking factors of 2-8 in microbenchmarks, not just 10-20%. (This is
   * a hotspot optimizer bug, which should be fixed, but that's a separate, big project).
   *
   * The coding pattern above is heavily used in java.util, e.g. in ArrayList. There is a

                "//tensorflow/core:framework_internal_impl",
                "//tensorflow/core/framework:tensor",
                "//tensorflow/core/grappler/optimizers:custom_graph_optimizer_registry_impl",
                "//tensorflow/core:lib_internal_impl",
                "//tensorflow/core/profiler:profiler_impl",

   *
   * The alternative natural refactorings into void or Exception-returning methods are much slower.
   * This is a big deal - we're talking factors of 2-8 in microbenchmarks, not just 10-20%. (This is
   * a hotspot optimizer bug, which should be fixed, but that's a separate, big project).
   *
   * The coding pattern above is heavily used in java.util, e.g. in ArrayList. There is a

    // Warning to maintainers: this implementation is highly optimized.
    int utf16Length = sequence.length();
    int utf8Length = utf16Length;
    int i = 0;

    // This loop optimizes for pure ASCII.
    while (i < utf16Length && sequence.charAt(i) < 0x80) {
      i++;
    }

    // This loop optimizes for chars less than 0x800.
    for (; i < utf16Length; i++) {
      char c = sequence.charAt(i);

    // Warning to maintainers: this implementation is highly optimized.
    int utf16Length = sequence.length();
    int utf8Length = utf16Length;
    int i = 0;

    // This loop optimizes for pure ASCII.
    while (i < utf16Length && sequence.charAt(i) < 0x80) {
      i++;
    }

    // This loop optimizes for chars less than 0x800.
    for (; i < utf16Length; i++) {
      char c = sequence.charAt(i);