code: def dropout(
      x: Union[tf.Tensor, tf.Variable],
      prob: float,
      /,
      *,
      scale: bool = True,
      dtype: tf.DType = None,
      training: bool = True,
      seed: Optional[int] = None,
      noise_shape: Optional[Sequence[int]] = None,
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      x = ivy.astype(x, dtype) if dtype and x.dtype != dtype else x
      if prob == 0 or not training:
          return x
      res = tf.nn.dropout(x, prob, noise_shape=noise_shape, seed=seed)
      res = tf.multiply(res, (1.0 - prob)) if not scale else res
      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 15 for calling a global function with example usage: dropout() and returns: res, x