google/generativeai/types/retriever_types.py [1289:1318]:
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            response = type(response).to_dict(response)
            return response
        if isinstance(chunks, Iterable) and not isinstance(chunks, Mapping):
            for chunk in chunks:
                if isinstance(chunk, protos.UpdateChunkRequest):
                    _requests.append(chunk)
                elif isinstance(chunk, tuple):
                    # First element is name of chunk, second element contains updates
                    chunk_to_update = self.get_chunk(name=chunk[0])

                    # Handle the custom_metadata parameter
                    c_data = []
                    if chunk_to_update.custom_metadata:
                        for cm in chunk_to_update.custom_metadata:
                            c_data.append(cm._to_proto())

                    # When handling updates, use to the _to_proto result of the custom_metadata
                    chunk_to_update.custom_metadata = c_data

                    updates = flatten_update_paths(chunk[1])
                    field_mask = field_mask_pb2.FieldMask()
                    for path in updates.keys():
                        field_mask.paths.append(path)
                    for path, value in updates.items():
                        chunk_to_update._apply_update(path, value)
                    _requests.append(
                        {"chunk": chunk_to_update.to_dict(), "update_mask": field_mask}
                    )
                else:
                    raise TypeError(
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google/generativeai/types/retriever_types.py [1379:1408]:
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            response = type(response).to_dict(response)
            return response
        if isinstance(chunks, Iterable) and not isinstance(chunks, Mapping):
            for chunk in chunks:
                if isinstance(chunk, protos.UpdateChunkRequest):
                    _requests.append(chunk)
                elif isinstance(chunk, tuple):
                    # First element is name of chunk, second element contains updates
                    chunk_to_update = self.get_chunk(name=chunk[0])

                    # Handle the custom_metadata parameter
                    c_data = []
                    if chunk_to_update.custom_metadata:
                        for cm in chunk_to_update.custom_metadata:
                            c_data.append(cm._to_proto())

                    # When handling updates, use to the _to_proto result of the custom_metadata
                    chunk_to_update.custom_metadata = c_data

                    updates = flatten_update_paths(chunk[1])
                    field_mask = field_mask_pb2.FieldMask()
                    for path in updates.keys():
                        field_mask.paths.append(path)
                    for path, value in updates.items():
                        chunk_to_update._apply_update(path, value)
                    _requests.append(
                        {"chunk": chunk_to_update.to_dict(), "update_mask": field_mask}
                    )
                else:
                    raise TypeError(
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