docstr: ivy.Container static 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
  ----------
  low
      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
  --------
  With :class:`ivy.Container` inputs:
  
  >>> 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]]))
  >>> ivy.Container.static_random_uniform(low=x, high=y, device='cpu',
  ...                                     dtype='float64')
  {
      a: ivy.array([[10.8, 23.7],
                    [17., 16.6]]),
      b: ivy.array([[2.35, 3.69],
                    [17.4, 48.]])
  }
  
  With a mix of :class:`ivy.Array` and :class:`ivy.Container` inputs:
  
  >>> x = ivy.array([-1.0,-9.0,-3.4])
  >>> y = ivy.Container(a=ivy.array([0.6, 0.2, 0.3]),b=ivy.array([0.8, 0.2, 0.2]))
  >>> ivy.Container.static_random_uniform(low=x, high=y)
  {
      a: ivy.array([0.481, -8.03, -2.74]),
      b: ivy.array([0.0999, -7.38, -1.29])
  }
docstr_tok: ["ivy.Container","static","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","--","--","--","--","--","low","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","--","--","--","--","With",":","class",":","`","ivy.Container","`","inputs",":",">",">",">","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","]","]",")",")",">",">",">","ivy.Container.static_random_uniform","(","low=x",",","high=y",",","device='cpu","'",",","...","dtype='float64","'",")","{","a",":","ivy.array","(","[","[","10.8",",","23.7","]",",","[","17.",",","16.6","]","]",")",",","b",":","ivy.array","(","[","[","2.35",",","3.69","]",",","[","17.4",",","48",".","]","]",")","}","With","a","mix","of",":","class",":","`","ivy.Array","`","and",":","class",":","`","ivy.Container","`","inputs",":",">",">",">","x","=","ivy.array","(","[","-1.0",",","-9.0",",","-3.4","]",")",">",">",">","y","=","ivy.Container","(","a=ivy.array","(","[","0.6",",","0.2",",","0.3","]",")",",","b=ivy.array","(","[","0.8",",","0.2",",","0.2","]",")",")",">",">",">","ivy.Container.static_random_uniform","(","low=x",",","high=y",")","{","a",":","ivy.array","(","[","0.481",",","-8.03",",","-2.74","]",")",",","b",":","ivy.array","(","[","0.0999",",","-7.38",",","-1.29","]",")","}"]
code: def _static_random_uniform(
      *,
      low: Union[
          float, ivy.Container, ivy.Array, ivy.NativeArray
      ] = 0.0,
      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 static 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
      ----------
      low
          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
      --------
      With :class:`ivy.Container` inputs:
  
      >>> 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]]))
      >>> ivy.Container.static_random_uniform(low=x, high=y, device='cpu',
      ...                                     dtype='float64')
      {
          a: ivy.array([[10.8, 23.7],
                        [17., 16.6]]),
          b: ivy.array([[2.35, 3.69],
                        [17.4, 48.]])
      }
  
      With a mix of :class:`ivy.Array` and :class:`ivy.Container` inputs:
  
      >>> x = ivy.array([-1.0,-9.0,-3.4])
      >>> y = ivy.Container(a=ivy.array([0.6, 0.2, 0.3]),b=ivy.array([0.8, 0.2, 0.2]))
      >>> ivy.Container.static_random_uniform(low=x, high=y)
      {
          a: ivy.array([0.481, -8.03, -2.74]),
          b: ivy.array([0.0999, -7.38, -1.29])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "random_uniform",
          low=low,
          high=high,
          shape=shape,
          device=device,
          dtype=dtype,
          seed=seed,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          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 1 for calling the _ContainerWithRandom obj's underlying member method code with example usage: obj._static_random_uniform() and returns: ContainerBase