parameters: (low=0, high=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs)
return_statement: return _random_helper(_internal._random_uniform, _internal._sample_uniform,
                            [low, high], shape, dtype, ctx, out, kwargs)
docstring: Draw random samples from a uniform distribution.
  
      Samples are uniformly distributed over the half-open interval *[low, high)*
      (includes *low*, but excludes *high*).
  
      Parameters
      ----------
      low : float or NDArray
          Lower boundary of the output interval. All values generated will be
          greater than or equal to low. The default value is 0.
      high : float or NDArray
          Upper boundary of the output interval. All values generated will be
          less than high. The default value is 1.0.
      shape : int or tuple of ints
          The number of samples to draw. If shape is, e.g., `(m, n)` and `low` and
          `high` are scalars, output shape will be `(m, n)`. If `low` and `high`
          are NDArrays with shape, e.g., `(x, y)`, then output will have shape
          `(x, y, m, n)`, where `m*n` samples are drawn for each `[low, high)` pair.
      dtype : {'float16','float32', 'float64'}
          Data type of output samples. Default is 'float32'
      ctx : Context
          Device context of output. Default is current context. Overridden by
          `low.context` when `low` is an NDArray.
      out : NDArray
          Store output to an existing NDArray.
  
  
      Examples
      --------
      >>> mx.nd.random.uniform(0, 1)
      [ 0.54881352]
      <NDArray 1 @cpu(0)
      >>>> mx.nd.random.uniform(0, 1, ctx=mx.gpu(0))
      [ 0.92514056]
      <NDArray 1 @gpu(0)>
      >>> mx.nd.random.uniform(-1, 1, shape=(2,))
      [[ 0.71589124  0.08976638]
       [ 0.69450343 -0.15269041]]
      <NDArray 2x2 @cpu(0)>
      >>> low = mx.nd.array([1,2,3])
      >>> high = mx.nd.array([2,3,4])
      >>> mx.nd.random.uniform(low, high, shape=2)
      [[ 1.78653979  1.93707538]
       [ 2.01311183  2.37081361]
       [ 3.30491424  3.69977832]]
      <NDArray 3x2 @cpu(0)>
docstring_summary: Draw random samples from a uniform distribution.
docstring_tokens: Draw random samples from a uniform distribution .
function: def uniform(low=0, high=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs):
      """Draw random samples from a uniform distribution.
  
      Samples are uniformly distributed over the half-open interval *[low, high)*
      (includes *low*, but excludes *high*).
  
      Parameters
      ----------
      low : float or NDArray
          Lower boundary of the output interval. All values generated will be
          greater than or equal to low. The default value is 0.
      high : float or NDArray
          Upper boundary of the output interval. All values generated will be
          less than high. The default value is 1.0.
      shape : int or tuple of ints
          The number of samples to draw. If shape is, e.g., `(m, n)` and `low` and
          `high` are scalars, output shape will be `(m, n)`. If `low` and `high`
          are NDArrays with shape, e.g., `(x, y)`, then output will have shape
          `(x, y, m, n)`, where `m*n` samples are drawn for each `[low, high)` pair.
      dtype : {'float16','float32', 'float64'}
          Data type of output samples. Default is 'float32'
      ctx : Context
          Device context of output. Default is current context. Overridden by
          `low.context` when `low` is an NDArray.
      out : NDArray
          Store output to an existing NDArray.
  
  
      Examples
      --------
      >>> mx.nd.random.uniform(0, 1)
      [ 0.54881352]
      <NDArray 1 @cpu(0)
      >>>> mx.nd.random.uniform(0, 1, ctx=mx.gpu(0))
      [ 0.92514056]
      <NDArray 1 @gpu(0)>
      >>> mx.nd.random.uniform(-1, 1, shape=(2,))
      [[ 0.71589124  0.08976638]
       [ 0.69450343 -0.15269041]]
      <NDArray 2x2 @cpu(0)>
      >>> low = mx.nd.array([1,2,3])
      >>> high = mx.nd.array([2,3,4])
      >>> mx.nd.random.uniform(low, high, shape=2)
      [[ 1.78653979  1.93707538]
       [ 2.01311183  2.37081361]
       [ 3.30491424  3.69977832]]
      <NDArray 3x2 @cpu(0)>
      """
      return _random_helper(_internal._random_uniform, _internal._sample_uniform,
                            [low, high], shape, dtype, ctx, out, kwargs)
function_tokens: def uniform ( low = 0 , high = 1 , shape = _Null , dtype = _Null , ctx = None , out = None , * * kwargs ) : return _random_helper ( _internal . _random_uniform , _internal . _sample_uniform , [ low , high ] , shape , dtype , ctx , out , kwargs )