code: def dropout1d(
      x: Union[tf.Tensor, tf.Variable],
      prob: float,
      /,
      *,
      training: bool = True,
      data_format: str = "NWC",
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      if training:
          is_batched = len(x.shape) == 3
          if data_format == "NCW":
              perm = (0, 2, 1) if is_batched else (1, 0)
              x = tf.transpose(x, perm)
          res = tf.nn.dropout(x, prob)
          if data_format == "NCW":
              res = tf.transpose(res, perm)
      else:
          res = x
      return res
  
imports: import math
  from typing import Union, Optional, Tuple, List, Literal, Sequence, Callable
  import tensorflow
  from ivy.func_wrapper import inputs_to_ivy_arrays, output_to_native_arrays, with_unsupported_dtypes, with_supported_dtypes, with_supported_device_and_dtypes
  from ..None import backend_version
  import ivy
  from ivy.functional.ivy.layers import _handle_padding, _get_num_padded_values, _validate_max_pool_params, _depth_max_pooling_helper
  from ivy.functional.ivy.experimental.layers import _padding_ceil_mode, _get_size
label_desc: Use image node_id 16 for calling a global function with example usage: dropout1d() and returns: res