docstr: ivy.Container instance method variant of ivy.random_normal. This
  method simply wraps the function, and so the docstring for
  ivy.random_normal also applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      The mean of the normal distribution to sample from. Default is ``0.0``.
  std
      The standard deviation of the normal distribution to sample from.
      Must be non-negative. Default is ``1.0``.
  shape
      If the given shape is, e.g ``(m, n, k)``, then ``m * n * k`` samples
      are drawn. Can only be specified when ``mean`` and ``std`` are numeric
      values, else exception will be raised.
      Default is ``None``, where a single value is returned.
  key_chains
      The key-chains to apply or not apply the method to. Default is ``None``.
  to_apply
      If True, the method will be applied to key_chains, otherwise key_chains
      will be skipped. Default is ``True``.
  prune_unapplied
      Whether to prune key_chains for which the function was not applied.
      Default is ``False``.
  map_sequences
      Whether to also map method to sequences (lists, tuples).
      Default is ``False``.
  device
      device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
      (Default value = None).
  dtype
      output array data type. If ``dtype`` is ``None``, the output array data
      type will be the default floating-point data type. Default ``None``
  seed
      A python integer. Used to create a random seed distribution
  out
      optional output array, for writing the result to. It must have a shape
      that the inputs broadcast to.
  
  Returns
  -------
  ret
      Drawn samples from the parameterized normal distribution.
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([7.5,6.7,0.9]),
  ...                   b=ivy.array([8.7,9.8,4.5]))
  >>> x.random_normal(std=17.4)
  {
      a: ivy.array([11.9, -22.9, -24.8]),
      b: ivy.array([44.3, -21.6, 2.03])
  }
  
  >>> x.random_normal(std=10.2, device='cpu')
  {
      a: ivy.array([7.82, 6.21, -0.431]),
      b: ivy.array([13.8, 9.9, 7.64])
  }
  
  >>> x.random_normal(std=14.2, dtype='float16')
  {
      a: ivy.array([-18.3, -3.42, 9.55]),
      b: ivy.array([-1.31, 7.68, -6.93])
  }
  
  >>> x.random_normal(std=10.8, device='cpu', dtype='float64')
  {
      a: ivy.array([13.4, -3.14, 10.7]),
      b: ivy.array([11.7, 4.85, 5.83])
  }
  
  >>> z = ivy.Container(a=ivy.zeros((3,)), b=ivy.ones((3,)))
  >>> x.random_normal(std=11.2, device='cpu', dtype='float64', out=z)
  {
      a: ivy.array([-6.84, 0.274, 14.2]),
      b: ivy.array([29.1, 7.19, 3.])
  }
  
  >>> y = ivy.Container(a=10.4, b=17.4)
  >>> x.random_normal(std=y)
  {
      a: ivy.array([-9.5, 8.54, -9.13]),
      b: ivy.array([-24.5, 18.9, 11.])
  }
  
  >>> x.random_normal(std=y, device='cpu')
  {
      a: ivy.array([8.47, 8.23, 8.69]),
      b: ivy.array([10.7, 16.2, 16.1])
  }
  
  >>> x.random_normal(std=y, dtype='float16')
  {
      a: ivy.array([8.22, -15.9, 10.4]),
      b: ivy.array([19.9, 11.5, -2.15])
  }
  
  >>> x.random_normal(std=y, device='cpu', dtype='float64')
  {
      a: ivy.array([19.6, -4.08, 6.09]),
      b: ivy.array([-23.9, 6.86, 17.6])
  }
  
  >>> z = ivy.Container(a=ivy.zeros((3,)), b=ivy.ones((3,)))
  >>> x.random_normal(std=y, device='cpu', dtype='float64', out=z)
  {
      a: ivy.array([14.7, 8.99, 8.46]),
      b: ivy.array([22.9, -5.97, -1.28])
  }
  
  >>> x = ivy.Container(a=ivy.array([[9.8,7.6],[6.5,2.3]]),
  ...                   b=ivy.array([[0.9,2.4],[7.6,5.4]]))
  >>> y = ivy.Container(a=ivy.array([[10.9,32.4],[18.7,19.6]]),
  ...                   b=ivy.array([[4.3,5.6],[23.4,54.3]]))
  >>> x.random_normal(std=y)
  {
      a: ivy.array([[10.6, 7.89],
                    [9.39, 19.4]]),
      b: ivy.array([[3.76, 4.68],
                    [17.7, 24.]])
  }
  
  >>> x.random_normal(std=y, device='cpu')
  {
      a: ivy.array([[30.9, 24.6],
                    [29.9, -25.3]]),
      b: ivy.array([[8.02, 1.92],
                    [-5.34, -54.1]])
  }
  
  >>> x.random_normal(std=y, dtype='float16')
  {
      a: ivy.array([[7.82, -35.],
                    [11.7, 0.696]]),
      b: ivy.array([[-4.07, -2.91],
                    [19.2, 46.8]])
  }
  
  >>> x.random_normal(std=y, device='cpu', dtype='float64')
  {
      a: ivy.array([[25.4, 28.3],
                    [19.6, -9.83]]),
      b: ivy.array([[2.95, 2.48],
                    [-30.8, -40.1]])
  }
  
  >>> z = ivy.Container(a=ivy.zeros((2,2)), b=ivy.ones((2,2)))
  >>> x.random_normal(std=y, device='cpu', dtype='float64', out=z)
  {
      a: ivy.array([[2.8, -45.6],
                    [-10.4, 0.65]]),
      b: ivy.array([[3.8, 1.43],
                    [23., 29.4]])
  }
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code: def random_normal(
      self: ivy.Container,
      /,
      *,
      std: Union[
          float, ivy.Container, ivy.Array, ivy.NativeArray
      ] = 1.0,
      shape: Optional[
          Union[ivy.Shape, ivy.NativeShape, ivy.Container]
      ] = None,
      key_chains: Optional[
          Union[List[str], Dict[str, str], ivy.Container]
      ] = None,
      to_apply: Union[bool, ivy.Container] = True,
      prune_unapplied: Union[bool, ivy.Container] = False,
      map_sequences: Union[bool, ivy.Container] = False,
      device: Optional[
          Union[ivy.Device, ivy.NativeDevice, ivy.Container]
      ] = None,
      dtype: Optional[
          Union[ivy.Dtype, ivy.NativeDtype, ivy.Container]
      ] = None,
      seed: Optional[Union[int, ivy.Container]] = None,
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container instance method variant of ivy.random_normal. This
      method simply wraps the function, and so the docstring for
      ivy.random_normal also applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          The mean of the normal distribution to sample from. Default is ``0.0``.
      std
          The standard deviation of the normal distribution to sample from.
          Must be non-negative. Default is ``1.0``.
      shape
          If the given shape is, e.g ``(m, n, k)``, then ``m * n * k`` samples
          are drawn. Can only be specified when ``mean`` and ``std`` are numeric
          values, else exception will be raised.
          Default is ``None``, where a single value is returned.
      key_chains
          The key-chains to apply or not apply the method to. Default is ``None``.
      to_apply
          If True, the method will be applied to key_chains, otherwise key_chains
          will be skipped. Default is ``True``.
      prune_unapplied
          Whether to prune key_chains for which the function was not applied.
          Default is ``False``.
      map_sequences
          Whether to also map method to sequences (lists, tuples).
          Default is ``False``.
      device
          device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
          (Default value = None).
      dtype
          output array data type. If ``dtype`` is ``None``, the output array data
          type will be the default floating-point data type. Default ``None``
      seed
          A python integer. Used to create a random seed distribution
      out
          optional output array, for writing the result to. It must have a shape
          that the inputs broadcast to.
  
      Returns
      -------
      ret
          Drawn samples from the parameterized normal distribution.
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([7.5,6.7,0.9]),
      ...                   b=ivy.array([8.7,9.8,4.5]))
      >>> x.random_normal(std=17.4)
      {
          a: ivy.array([11.9, -22.9, -24.8]),
          b: ivy.array([44.3, -21.6, 2.03])
      }
  
      >>> x.random_normal(std=10.2, device='cpu')
      {
          a: ivy.array([7.82, 6.21, -0.431]),
          b: ivy.array([13.8, 9.9, 7.64])
      }
  
      >>> x.random_normal(std=14.2, dtype='float16')
      {
          a: ivy.array([-18.3, -3.42, 9.55]),
          b: ivy.array([-1.31, 7.68, -6.93])
      }
  
      >>> x.random_normal(std=10.8, device='cpu', dtype='float64')
      {
          a: ivy.array([13.4, -3.14, 10.7]),
          b: ivy.array([11.7, 4.85, 5.83])
      }
  
      >>> z = ivy.Container(a=ivy.zeros((3,)), b=ivy.ones((3,)))
      >>> x.random_normal(std=11.2, device='cpu', dtype='float64', out=z)
      {
          a: ivy.array([-6.84, 0.274, 14.2]),
          b: ivy.array([29.1, 7.19, 3.])
      }
  
      >>> y = ivy.Container(a=10.4, b=17.4)
      >>> x.random_normal(std=y)
      {
          a: ivy.array([-9.5, 8.54, -9.13]),
          b: ivy.array([-24.5, 18.9, 11.])
      }
  
      >>> x.random_normal(std=y, device='cpu')
      {
          a: ivy.array([8.47, 8.23, 8.69]),
          b: ivy.array([10.7, 16.2, 16.1])
      }
  
      >>> x.random_normal(std=y, dtype='float16')
      {
          a: ivy.array([8.22, -15.9, 10.4]),
          b: ivy.array([19.9, 11.5, -2.15])
      }
  
      >>> x.random_normal(std=y, device='cpu', dtype='float64')
      {
          a: ivy.array([19.6, -4.08, 6.09]),
          b: ivy.array([-23.9, 6.86, 17.6])
      }
  
      >>> z = ivy.Container(a=ivy.zeros((3,)), b=ivy.ones((3,)))
      >>> x.random_normal(std=y, device='cpu', dtype='float64', out=z)
      {
          a: ivy.array([14.7, 8.99, 8.46]),
          b: ivy.array([22.9, -5.97, -1.28])
      }
  
      >>> x = ivy.Container(a=ivy.array([[9.8,7.6],[6.5,2.3]]),
      ...                   b=ivy.array([[0.9,2.4],[7.6,5.4]]))
      >>> y = ivy.Container(a=ivy.array([[10.9,32.4],[18.7,19.6]]),
      ...                   b=ivy.array([[4.3,5.6],[23.4,54.3]]))
      >>> x.random_normal(std=y)
      {
          a: ivy.array([[10.6, 7.89],
                        [9.39, 19.4]]),
          b: ivy.array([[3.76, 4.68],
                        [17.7, 24.]])
      }
  
      >>> x.random_normal(std=y, device='cpu')
      {
          a: ivy.array([[30.9, 24.6],
                        [29.9, -25.3]]),
          b: ivy.array([[8.02, 1.92],
                        [-5.34, -54.1]])
      }
  
      >>> x.random_normal(std=y, dtype='float16')
      {
          a: ivy.array([[7.82, -35.],
                        [11.7, 0.696]]),
          b: ivy.array([[-4.07, -2.91],
                        [19.2, 46.8]])
      }
  
      >>> x.random_normal(std=y, device='cpu', dtype='float64')
      {
          a: ivy.array([[25.4, 28.3],
                        [19.6, -9.83]]),
          b: ivy.array([[2.95, 2.48],
                        [-30.8, -40.1]])
      }
  
      >>> z = ivy.Container(a=ivy.zeros((2,2)), b=ivy.ones((2,2)))
      >>> x.random_normal(std=y, device='cpu', dtype='float64', out=z)
      {
          a: ivy.array([[2.8, -45.6],
                        [-10.4, 0.65]]),
          b: ivy.array([[3.8, 1.43],
                        [23., 29.4]])
      }
      """
      return self._static_random_normal(
          mean=self,
          std=std,
          shape=shape,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          device=device,
          dtype=dtype,
          seed=seed,
          out=out,
      )
  
file_path: ivy/ivy/data_classes/container/random.py
imports: from typing import Optional, Union, List, Dict
  import ivy
  from ivy.data_classes.container.base import ContainerBase
label_desc: Use image node_id 4 for calling the _ContainerWithRandom obj's underlying member method code with example usage: obj.random_normal() and returns: self