parameters: (n=1, p=0.5, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs)
return_statement: return _random_helper(_internal._random_binomial, _internal._sample_binomial,
                            [n, p], shape, dtype, ctx, out, kwargs)
docstring: Draw random samples from a binomial distribution.
  
      Samples are distributed according to a binomial distribution parametrized
      by *n* (number of trials) and *p* (success probability).
  
      Parameters
      ----------
      n : float or NDArray, optional
          Number of experiments, > 0.
      p : float or NDArray, optional
          Success probability in each experiment, >= 0 and <= 1.
      shape : int or tuple of ints, optional
          The number of samples to draw. If shape is, e.g., `(m, n)` and `n` and
          `p` are scalars, output shape will be `(m, n)`. If `n` and `p`
          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 `[n, p)` pair.
      dtype : {'float16', 'float32', 'float64'}, optional
          Data type of output samples. Default is 'float32'
      ctx : Context, optional
          Device context of output. Default is current context. Overridden by
          `n.context` when `n` is an NDArray.
      out : NDArray, optional
          Store output to an existing NDArray.
  
      Returns
      -------
      NDArray
          If input `shape` has shape, e.g., `(m, n)` and `n` and `p` are scalars, output
          shape will be `(m, n)`. If `n` and `p` 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 `[n, p)` pair.
  
      Examples
      --------
      >>> mx.nd.random.binomial(10, 0.1)
      [ 1.]
      <NDArray 1 @cpu(0)>
      >>> mx.nd.random.binomial(10, 0.6, shape=(2,))
      [ 4. 6.]
      <NDArray 2 @cpu(0)>
      >>> n = mx.nd.array([10,2,3])
      >>> p = mx.nd.array([0.2,0.3,0.4])
      >>> mx.nd.random.binomial(n, p, shape=2)
      [[  1. 4.]
       [  0. 2.]
       [  1. 1.]]
      <NDArray 3x2 @cpu(0)>
docstring_summary: Draw random samples from a binomial distribution.
docstring_tokens: Draw random samples from a binomial distribution .
function: def binomial(n=1, p=0.5, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs):
      """Draw random samples from a binomial distribution.
  
      Samples are distributed according to a binomial distribution parametrized
      by *n* (number of trials) and *p* (success probability).
  
      Parameters
      ----------
      n : float or NDArray, optional
          Number of experiments, > 0.
      p : float or NDArray, optional
          Success probability in each experiment, >= 0 and <= 1.
      shape : int or tuple of ints, optional
          The number of samples to draw. If shape is, e.g., `(m, n)` and `n` and
          `p` are scalars, output shape will be `(m, n)`. If `n` and `p`
          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 `[n, p)` pair.
      dtype : {'float16', 'float32', 'float64'}, optional
          Data type of output samples. Default is 'float32'
      ctx : Context, optional
          Device context of output. Default is current context. Overridden by
          `n.context` when `n` is an NDArray.
      out : NDArray, optional
          Store output to an existing NDArray.
  
      Returns
      -------
      NDArray
          If input `shape` has shape, e.g., `(m, n)` and `n` and `p` are scalars, output
          shape will be `(m, n)`. If `n` and `p` 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 `[n, p)` pair.
  
      Examples
      --------
      >>> mx.nd.random.binomial(10, 0.1)
      [ 1.]
      <NDArray 1 @cpu(0)>
      >>> mx.nd.random.binomial(10, 0.6, shape=(2,))
      [ 4. 6.]
      <NDArray 2 @cpu(0)>
      >>> n = mx.nd.array([10,2,3])
      >>> p = mx.nd.array([0.2,0.3,0.4])
      >>> mx.nd.random.binomial(n, p, shape=2)
      [[  1. 4.]
       [  0. 2.]
       [  1. 1.]]
      <NDArray 3x2 @cpu(0)>
      """
      return _random_helper(_internal._random_binomial, _internal._sample_binomial,
                            [n, p], shape, dtype, ctx, out, kwargs)
function_tokens: def binomial ( n = 1 , p = 0.5 , shape = _Null , dtype = _Null , ctx = None , out = None , * * kwargs ) : return _random_helper ( _internal . _random_binomial , _internal . _sample_binomial , [ n , p ] , shape , dtype , ctx , out , kwargs )