parameters: (loc=0, scale=1, shape=_Null, dtype=_Null, **kwargs)
return_statement: return _random_helper(_internal._random_normal, _internal._sample_normal,
                            [loc, scale], shape, dtype, kwargs)
docstring: Draw random samples from a normal (Gaussian) distribution.
  
      Samples are distributed according to a normal distribution parametrized
      by *loc* (mean) and *scale* (standard deviation).
  
  
      Parameters
      ----------
      loc : float or Symbol, optional
          Mean (centre) of the distribution.
      scale : float or Symbol, optional
          Standard deviation (spread or width) of the distribution.
      shape : int or tuple of ints, optional
          The number of samples to draw. If shape is, e.g., `(m, n)` and `loc` and
          `scale` are scalars, output shape will be `(m, n)`. If `loc` and `scale`
          are Symbols with shape, e.g., `(x, y)`, then output will have shape
          `(x, y, m, n)`, where `m*n` samples are drawn for each `[loc, scale)` pair.
      dtype : {'float16', 'float32', 'float64'}, optional
          Data type of output samples. Default is 'float32'
  
      Returns
      -------
      Symbol
          If input `shape` has dimensions, e.g., `(m, n)`, and `loc` and
          `scale` are scalars, returned Symbol will resolve to shape `(m, n)`.
          If `loc` and `scale` are Symbols with shape, e.g., `(x, y)`, returned
          Symbol will resolve to shape `(x, y, m, n)`, where `m*n` samples are drawn
          for each `[loc, scale)` pair.
docstring_summary: Draw random samples from a normal (Gaussian) distribution.
docstring_tokens: Draw random samples from a normal ( Gaussian ) distribution .
function: def normal(loc=0, scale=1, shape=_Null, dtype=_Null, **kwargs):
      """Draw random samples from a normal (Gaussian) distribution.
  
      Samples are distributed according to a normal distribution parametrized
      by *loc* (mean) and *scale* (standard deviation).
  
  
      Parameters
      ----------
      loc : float or Symbol, optional
          Mean (centre) of the distribution.
      scale : float or Symbol, optional
          Standard deviation (spread or width) of the distribution.
      shape : int or tuple of ints, optional
          The number of samples to draw. If shape is, e.g., `(m, n)` and `loc` and
          `scale` are scalars, output shape will be `(m, n)`. If `loc` and `scale`
          are Symbols with shape, e.g., `(x, y)`, then output will have shape
          `(x, y, m, n)`, where `m*n` samples are drawn for each `[loc, scale)` pair.
      dtype : {'float16', 'float32', 'float64'}, optional
          Data type of output samples. Default is 'float32'
  
      Returns
      -------
      Symbol
          If input `shape` has dimensions, e.g., `(m, n)`, and `loc` and
          `scale` are scalars, returned Symbol will resolve to shape `(m, n)`.
          If `loc` and `scale` are Symbols with shape, e.g., `(x, y)`, returned
          Symbol will resolve to shape `(x, y, m, n)`, where `m*n` samples are drawn
          for each `[loc, scale)` pair.
      """
      return _random_helper(_internal._random_normal, _internal._sample_normal,
                            [loc, scale], shape, dtype, kwargs)
function_tokens: def normal ( loc = 0 , scale = 1 , shape = _Null , dtype = _Null , * * kwargs ) : return _random_helper ( _internal . _random_normal , _internal . _sample_normal , [ loc , scale ] , shape , dtype , kwargs )