docstr: ivy.Container instance method variant of ivy.random_uniform. This
  method simply wraps the function, and so the docstring for
  ivy.random_uniform also applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      Lower boundary of the output interval. All values generated will be
      greater than or equal to ``low``. If array, must have same shape as
      ``high``.
  high
      Upper boundary of the output interval. All the values generated will be
      less than ``high``. If array, must have same shape as ``low``.
  shape
      If the given shape is, e.g ``(m, n, k)``, then ``m * n * k`` samples
      are drawn. Can only be specified when ``low`` and ``high`` 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 uniform 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_uniform(high=17.4)
  {
      a: ivy.array([11.2, 10.5, 13.1]),
      b: ivy.array([11.2, 11.9, 6.01])
  }
  
  >>> x.random_uniform(high=10.2, device='cpu')
  {
      a: ivy.array([8.55, 10.1, 4.08]),
      b: ivy.array([9.45, 9.9, 8.6])
  }
  
  >>> x.random_uniform(high=14.2, dtype='float16')
  {
      a: ivy.array([12.4, 11.7, 7.25]),
      b: ivy.array([11.8, 11.8, 4.96])
  }
  
  >>> x.random_uniform(high=10.8, device='cpu', dtype='float64')
  {
      a: ivy.array([8.86, 9.24, 6.43]),
      b: ivy.array([8.95, 10.1, 8.51])
  }
  
  >>> z = ivy.Container(a=ivy.zeros((3,)), b=ivy.ones((3,)))
  >>> x.random_uniform(high=11.2, device='cpu', dtype='float64', out=z)
  {
      a: ivy.array([9.6, 8.24, 3.67]),
      b: ivy.array([9.29, 11.2, 9.84])
  }
  
  >>> y = ivy.Container(a=10.4, b=17.4)
  >>> x.random_uniform(high=y)
  {
      a: ivy.array([8.24, 9.22, 1.52]),
      b: ivy.array([16.5, 13.4, 17.3])
  }
  
  >>> x.random_uniform(high=y, device='cpu')
  {
      a: ivy.array([8.55, 10.1, 4.08]),
      b: ivy.array([9.45, 9.9, 8.6])
  }
  
  >>> x.random_uniform(high=y, dtype='float16')
  {
      a: ivy.array([12.4, 11.7, 7.25]),
      b: ivy.array([11.8, 11.8, 4.96])
  }
  
  >>> x.random_uniform(high=y, device='cpu', dtype='float64')
  {
      a: ivy.array([8.86, 9.24, 6.43]),
      b: ivy.array([8.95, 10.1, 8.51])
  }
  
  >>> z = ivy.Container(a=ivy.zeros((3,)), b=ivy.ones((3,)))
  >>> x.random_uniform(high=y, device='cpu', dtype='float64', out=z)
  {
      a: ivy.array([9.6, 8.24, 3.67]),
      b: ivy.array([9.29, 11.2, 9.84])
  }
  
  >>> 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_uniform(high=y)
  {
      a: ivy.array([[10.4, 17.],
                    [9.81, 10.9]]),
      b: ivy.array([[3.6, 4.31],
                    [18.8, 54.2]])
  }
  
  >>> x.random_uniform(high=y, device='cpu')
  {
      a: ivy.array([[10.1, 7.93],
                    [7.98, 6.]]),
      b: ivy.array([[4.28, 4.65],
                    [13.9, 28.9]])
  }
  
  >>> x.random_uniform(high=y, dtype='float16')
  {
      a: ivy.array([[10.6, 28.],
                    [16.4, 4.92]]),
      b: ivy.array([[3.61, 4.82],
                    [12.6, 10.2]])
  }
  
  >>> x.random_uniform(high=y, device='cpu', dtype='float64')
  {
      a: ivy.array([[10.7, 28.4],
                    [9.29, 17.4]]),
      b: ivy.array([[1.88, 4.94],
                    [17., 9.68]])
  }
  
  >>> z = ivy.Container(a=ivy.zeros((2,2)), b=ivy.ones((2,2)))
  >>> x.random_uniform(high=y, device='cpu', dtype='float64', out=z)
  {
      a: ivy.array([[10.4, 29.8],
                    [12.1, 3.9]]),
      b: ivy.array([[3.79, 5.4],
                    [16.2, 31.7]])
  }
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code: def random_uniform(
      self: ivy.Container,
      /,
      *,
      high: 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_uniform. This
      method simply wraps the function, and so the docstring for
      ivy.random_uniform also applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          Lower boundary of the output interval. All values generated will be
          greater than or equal to ``low``. If array, must have same shape as
          ``high``.
      high
          Upper boundary of the output interval. All the values generated will be
          less than ``high``. If array, must have same shape as ``low``.
      shape
          If the given shape is, e.g ``(m, n, k)``, then ``m * n * k`` samples
          are drawn. Can only be specified when ``low`` and ``high`` 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 uniform 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_uniform(high=17.4)
      {
          a: ivy.array([11.2, 10.5, 13.1]),
          b: ivy.array([11.2, 11.9, 6.01])
      }
  
      >>> x.random_uniform(high=10.2, device='cpu')
      {
          a: ivy.array([8.55, 10.1, 4.08]),
          b: ivy.array([9.45, 9.9, 8.6])
      }
  
      >>> x.random_uniform(high=14.2, dtype='float16')
      {
          a: ivy.array([12.4, 11.7, 7.25]),
          b: ivy.array([11.8, 11.8, 4.96])
      }
  
      >>> x.random_uniform(high=10.8, device='cpu', dtype='float64')
      {
          a: ivy.array([8.86, 9.24, 6.43]),
          b: ivy.array([8.95, 10.1, 8.51])
      }
  
      >>> z = ivy.Container(a=ivy.zeros((3,)), b=ivy.ones((3,)))
      >>> x.random_uniform(high=11.2, device='cpu', dtype='float64', out=z)
      {
          a: ivy.array([9.6, 8.24, 3.67]),
          b: ivy.array([9.29, 11.2, 9.84])
      }
  
      >>> y = ivy.Container(a=10.4, b=17.4)
      >>> x.random_uniform(high=y)
      {
          a: ivy.array([8.24, 9.22, 1.52]),
          b: ivy.array([16.5, 13.4, 17.3])
      }
  
      >>> x.random_uniform(high=y, device='cpu')
      {
          a: ivy.array([8.55, 10.1, 4.08]),
          b: ivy.array([9.45, 9.9, 8.6])
      }
  
      >>> x.random_uniform(high=y, dtype='float16')
      {
          a: ivy.array([12.4, 11.7, 7.25]),
          b: ivy.array([11.8, 11.8, 4.96])
      }
  
      >>> x.random_uniform(high=y, device='cpu', dtype='float64')
      {
          a: ivy.array([8.86, 9.24, 6.43]),
          b: ivy.array([8.95, 10.1, 8.51])
      }
  
      >>> z = ivy.Container(a=ivy.zeros((3,)), b=ivy.ones((3,)))
      >>> x.random_uniform(high=y, device='cpu', dtype='float64', out=z)
      {
          a: ivy.array([9.6, 8.24, 3.67]),
          b: ivy.array([9.29, 11.2, 9.84])
      }
  
      >>> 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_uniform(high=y)
      {
          a: ivy.array([[10.4, 17.],
                        [9.81, 10.9]]),
          b: ivy.array([[3.6, 4.31],
                        [18.8, 54.2]])
      }
  
      >>> x.random_uniform(high=y, device='cpu')
      {
          a: ivy.array([[10.1, 7.93],
                        [7.98, 6.]]),
          b: ivy.array([[4.28, 4.65],
                        [13.9, 28.9]])
      }
  
      >>> x.random_uniform(high=y, dtype='float16')
      {
          a: ivy.array([[10.6, 28.],
                        [16.4, 4.92]]),
          b: ivy.array([[3.61, 4.82],
                        [12.6, 10.2]])
      }
  
      >>> x.random_uniform(high=y, device='cpu', dtype='float64')
      {
          a: ivy.array([[10.7, 28.4],
                        [9.29, 17.4]]),
          b: ivy.array([[1.88, 4.94],
                        [17., 9.68]])
      }
  
      >>> z = ivy.Container(a=ivy.zeros((2,2)), b=ivy.ones((2,2)))
      >>> x.random_uniform(high=y, device='cpu', dtype='float64', out=z)
      {
          a: ivy.array([[10.4, 29.8],
                        [12.1, 3.9]]),
          b: ivy.array([[3.79, 5.4],
                        [16.2, 31.7]])
      }
      """
      return self._static_random_uniform(
          low=self,
          high=high,
          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 2 for calling the _ContainerWithRandom obj's underlying member method code with example usage: obj.random_uniform() and returns: self