docstr: ivy.Array instance method variant of ivy.dropout. This method simply
  wraps the function, and so the docstring for ivy.dropout also applies
  to this method with minimal changes.
  
  Parameters
  ----------
  self
      The input array x to perform dropout on.
  prob
      The probability of zeroing out each array element, float between 0 and 1.
  scale
      Whether to scale the output by `1/(1-prob)`, default is ``True``.
  dtype
      output array data type. If dtype is None, the output array data type
      must be inferred from x. Default: ``None``.
  training
      Turn on dropout if training, turn off otherwise. Default is ``True``.
  seed
      Set a default seed for random number generating (for
      reproducibility).Default is ``None``.
  noise_shape
      a sequence representing the shape of the binary dropout mask that will be
      multiplied with the input.
  out
      optional output array, for writing the result to. It must have
      a shape that the inputs broadcast to.
  
  Returns
  -------
  ret
      Result array of the output after dropout is performed.
  
  Examples
  --------
  With :class:`ivy.Array` instances:
  
  >>> x = ivy.array([[1., 2., 3.],
  ...                [4., 5., 6.],
  ...                [7., 8., 9.],
  ...                [10., 11., 12.]])
  >>> y = x.dropout(0.3)
  >>> print(y)
  ivy.array([[ 1.42857146,  2.85714293,  4.28571415],
             [ 5.71428585,  7.14285755,  8.5714283 ],
             [ 0.        , 11.4285717 , 12.8571434 ],
             [14.2857151 ,  0.        ,  0.        ]])
  
  >>> x = ivy.array([[1., 2., 3.],
  ...                [4., 5., 6.],
  ...                [7., 8., 9.],
  ...                [10., 11., 12.]])
  >>> y = x.dropout(0.3, scale=False)
  >>> print(y)
  ivy.array([[ 1.,  2., 3.],
             [ 4.,  5., 0.],
             [ 7.,  0., 9.],
             [10., 11., 0.]])
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code: def dropout(
      self: ivy.Array,
      prob: float,
      /,
      *,
      scale: bool = True,
      dtype: Optional[Union[ivy.Dtype, ivy.NativeDtype]] = None,
      training: bool = True,
      seed: Optional[int] = None,
      noise_shape: Optional[Sequence[int]] = None,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array instance method variant of ivy.dropout. This method simply
      wraps the function, and so the docstring for ivy.dropout also applies
      to this method with minimal changes.
  
      Parameters
      ----------
      self
          The input array x to perform dropout on.
      prob
          The probability of zeroing out each array element, float between 0 and 1.
      scale
          Whether to scale the output by `1/(1-prob)`, default is ``True``.
      dtype
          output array data type. If dtype is None, the output array data type
          must be inferred from x. Default: ``None``.
      training
          Turn on dropout if training, turn off otherwise. Default is ``True``.
      seed
          Set a default seed for random number generating (for
          reproducibility).Default is ``None``.
      noise_shape
          a sequence representing the shape of the binary dropout mask that will be
          multiplied with the input.
      out
          optional output array, for writing the result to. It must have
          a shape that the inputs broadcast to.
  
      Returns
      -------
      ret
          Result array of the output after dropout is performed.
  
      Examples
      --------
      With :class:`ivy.Array` instances:
  
      >>> x = ivy.array([[1., 2., 3.],
      ...                [4., 5., 6.],
      ...                [7., 8., 9.],
      ...                [10., 11., 12.]])
      >>> y = x.dropout(0.3)
      >>> print(y)
      ivy.array([[ 1.42857146,  2.85714293,  4.28571415],
                 [ 5.71428585,  7.14285755,  8.5714283 ],
                 [ 0.        , 11.4285717 , 12.8571434 ],
                 [14.2857151 ,  0.        ,  0.        ]])
  
      >>> x = ivy.array([[1., 2., 3.],
      ...                [4., 5., 6.],
      ...                [7., 8., 9.],
      ...                [10., 11., 12.]])
      >>> y = x.dropout(0.3, scale=False)
      >>> print(y)
      ivy.array([[ 1.,  2., 3.],
                 [ 4.,  5., 0.],
                 [ 7.,  0., 9.],
                 [10., 11., 0.]])
      """
      return ivy.dropout(
          self._data,
          prob,
          scale=scale,
          dtype=dtype,
          training=training,
          seed=seed,
          noise_shape=noise_shape,
          out=out,
      )
  
file_path: ivy/ivy/data_classes/array/layers.py
imports: import abc
  from typing import Optional, Tuple, Union, List, Sequence
  import ivy
label_desc: Use image node_id 2 for calling the _ArrayWithLayers obj's underlying member method code with example usage: obj.dropout() and returns: ivy