code: def pad(
      input: Union[tf.Tensor, tf.Variable],
      pad_width: Union[Iterable[Tuple[int]], int],
      /,
      *,
      mode: Union[
          Literal[
              "constant",
              "dilated",
              "edge",
              "linear_ramp",
              "maximum",
              "mean",
              "median",
              "minimum",
              "reflect",
              "symmetric",
              "wrap",
              "empty",
          ],
          Callable,
      ] = "constant",
      stat_length: Union[Iterable[Tuple[int]], int] = 1,
      constant_values: Union[Iterable[Tuple[Number]], Number] = 0,
      end_values: Union[Iterable[Tuple[Number]], Number] = 0,
      reflect_type: Literal["even", "odd"] = "even",
      **kwargs: Optional[Any],
  ) -> Union[tf.Tensor, tf.Variable]:
      pad_width = _to_tf_padding(pad_width, len(input.shape))
      if isinstance(constant_values, (tf.Variable, tf.Tensor)):
          if constant_values.dtype != input.dtype:
              constant_values = tf.cast(constant_values, input.dtype)
      return tf.pad(
          input,
          pad_width,
          mode=mode,
          constant_values=constant_values,
      )
  
imports: from collections import namedtuple
  from typing import Iterable, Union, Optional, Sequence, Tuple, NamedTuple, List, Literal, Callable, Any
  from numbers import Number
  import tensorflow
  from ivy.func_wrapper import with_unsupported_dtypes
  from ..None import backend_version
  import ivy
  from ivy.functional.ivy.experimental.manipulation import _to_tf_padding
label_desc: Use image node_id 19 for calling a global function with example usage: pad() and returns: tf