code: def pad(
      input: JaxArray,
      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],
  ) -> JaxArray:
      pad_width = _to_nested_tuple(pad_width)
      stat_length = _to_nested_tuple(stat_length)
      constant_values = _to_nested_tuple(constant_values)
      end_values = _to_nested_tuple(end_values)
      input_dtype = input.dtype
  
      if mode == "dilated":
          if (
              not ivy.is_array(constant_values)
              or constant_values.dtype != input_dtype
          ):
              constant_values = jnp.array(
                  constant_values, dtype=input_dtype
              )
          return jlax.pad(input, constant_values, pad_width)
      if callable(mode):
          ret = jnp.pad(
              _flat_array_to_1_dim_array(input),
              pad_width,
              mode=mode,
              **kwargs,
          )
      elif mode in ["maximum", "mean", "median", "minimum"]:
          ret = jnp.pad(
              _flat_array_to_1_dim_array(input),
              pad_width,
              mode=mode,
              stat_length=stat_length,
          )
      elif mode == "constant":
          ret = jnp.pad(
              _flat_array_to_1_dim_array(input),
              pad_width,
              mode=mode,
              constant_values=constant_values,
          )
      elif mode == "linear_ramp":
          ret = jnp.pad(
              _flat_array_to_1_dim_array(input),
              pad_width,
              mode=mode,
              end_values=end_values,
          )
      elif mode in ["reflect", "symmetric"]:
          ret = jnp.pad(
              _flat_array_to_1_dim_array(input),
              pad_width,
              mode=mode,
              reflect_type=reflect_type,
          )
      else:
          ret = jnp.pad(
              _flat_array_to_1_dim_array(input),
              pad_width,
              mode=mode,
          )
      if jnp.issubdtype(input_dtype, jnp.integer) and mode in [
          "mean",
          "median",
      ]:
          ret = jnp.round(ret).astype(input_dtype)
      return ret
  
imports: from typing import Iterable, Optional, Union, Sequence, Tuple, NamedTuple, Literal, Callable, Any, List
  import jax.numpy
  import jax.lax
  from numbers import Number
  from collections import namedtuple
  import ivy
  from ivy.functional.backends.jax import JaxArray
label_desc: Use image node_id 12 for calling a global function with example usage: pad() and returns: ret, jlax