def _get_resized_lm_head_bias(self, old_lm_head_bias, new_num_tokens):
        """
        Build a resized bias from the old ones. Increasing the size will add newly initialized vectors at the end.
        Reducing the size will remove vectors from the end

        Args:
            old_lm_head_bias (`tf.Variable`):
                Old lm head bias to be resized.
            new_num_tokens (`int`, *optional*):
                New number of tokens in the linear matrix.

                Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
                vectors from the end. If not provided or `None`, just returns None

        Return:
            `tf.Variable`: Pointer to the resized bias.
        """
        # TODO (joao): flagged for replacement (by `_v2_get_resized_lm_head_bias`) due to embeddings refactor
        new_lm_head_bias = {}

        for attr, weight in old_lm_head_bias.items():
            first_dim, old_num_tokens = (None, shape_list(weight)[0]) if tf.rank(weight) == 1 else shape_list(weight)
            size_diff = new_num_tokens - old_num_tokens
            final_shape = [new_num_tokens] if first_dim is None else [first_dim, new_num_tokens]

            # initialize new bias
            if tf.math.greater(size_diff, 0):
                padding_shape = [[0, size_diff]] if first_dim is None else [[0, 0], [0, size_diff]]
                current_bias = tf.pad(weight.value(), tf.convert_to_tensor(padding_shape), constant_values=-1)
                num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
                mask_shape = [num_tokens_to_copy] if first_dim is None else [1, num_tokens_to_copy]
                bias_mask = tf.fill(tf.convert_to_tensor(mask_shape), True)
                bias_mask = tf.pad(bias_mask, tf.convert_to_tensor(padding_shape), constant_values=False)
            else:
                slice_from = [0] if first_dim is None else [0, 0]
                current_bias = tf.slice(
                    weight.value(), tf.convert_to_tensor(slice_from), tf.convert_to_tensor(final_shape)
                )
                bias_mask = tf.fill(tf.convert_to_tensor(final_shape), True)

            new_bias = self.add_weight(
                shape=final_shape,
                initializer="zeros",
                trainable=True,
                name=weight.name.split(":")[0],
            )
            init_bias = tf.where(bias_mask, current_bias, new_bias.value())

            new_bias.assign(init_bias)
            new_lm_head_bias[attr] = new_bias

        return new_lm_head_bias

        Build a resized bias from the old ones. Increasing the size will add newly initialized vectors at the end.
        Reducing the size will remove vectors from the end

        Args:
            old_lm_head_bias (`tf.Variable`):
                Old lm head bias to be resized.
            new_num_tokens (`int`, *optional*):
                New number of tokens in the linear matrix.

                Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
                vectors from the end. If not provided or `None`, just returns None

        Return:
            `tf.Variable`: Pointer to the resized bias.
        
dpkd/transformers/src/transformers/modeling_tf_utils.py