parameters: (shape, p=0.0, dtype=None, seed=None)
return_statement: return array_ops.where(
        random_ops.random_uniform(shape, dtype=dtype, seed=seed) <= p,
        array_ops.ones(shape, dtype=dtype), array_ops.zeros(shape, dtype=dtype))
docstring: Returns a tensor with random binomial distribution of values.
  
    Arguments:
        shape: A tuple of integers, the shape of tensor to create.
        p: A float, `0. <= p <= 1`, probability of binomial distribution.
        dtype: String, dtype of returned tensor.
        seed: Integer, random seed.
  
    Returns:
        A tensor.
docstring_summary: Returns a tensor with random binomial distribution of values.
docstring_tokens: Returns a tensor with random binomial distribution of values .
function: def random_binomial(shape, p=0.0, dtype=None, seed=None):
    """Returns a tensor with random binomial distribution of values.
  
    Arguments:
        shape: A tuple of integers, the shape of tensor to create.
        p: A float, `0. <= p <= 1`, probability of binomial distribution.
        dtype: String, dtype of returned tensor.
        seed: Integer, random seed.
  
    Returns:
        A tensor.
    """
    if dtype is None:
      dtype = floatx()
    if seed is None:
      seed = np.random.randint(10e6)
    return array_ops.where(
        random_ops.random_uniform(shape, dtype=dtype, seed=seed) <= p,
        array_ops.ones(shape, dtype=dtype), array_ops.zeros(shape, dtype=dtype))
function_tokens: def random_binomial ( shape , p = 0.0 , dtype = None , seed = None ) : if dtype is None : dtype = floatx ( ) if seed is None : seed = np . random . randint ( 10e6 ) return array_ops . where ( random_ops . random_uniform ( shape , dtype = dtype , seed = seed ) <= p , array_ops . ones ( shape , dtype = dtype ) , array_ops . zeros ( shape , dtype = dtype ) )