parameters: (shape, mean, scale, dtype=None, name=None,
                             seed=None)
return_statement: return variable(value, dtype=dtype, name=name)
docstring: Instantiates a variable with values drawn from a normal distribution.
  
    Arguments:
        shape: Tuple of integers, shape of returned Keras variable.
        mean: Float, mean of the normal distribution.
        scale: Float, standard deviation of the normal distribution.
        dtype: String, dtype of returned Keras variable.
        name: String, name of returned Keras variable.
        seed: Integer, random seed.
  
    Returns:
        A Keras variable, filled with drawn samples.
  
    Example:
    ```python
        # TensorFlow example
        >>> kvar = K.random_normal_variable((2,3), 0, 1)
        >>> kvar
        <tensorflow.python.ops.variables.Variable object at 0x10ab12dd0>
        >>> K.eval(kvar)
        array([[ 1.19591331,  0.68685907, -0.63814116],
               [ 0.92629528,  0.28055015,  1.70484698]], dtype=float32)
    ```
docstring_summary: Instantiates a variable with values drawn from a normal distribution.
docstring_tokens: Instantiates a variable with values drawn from a normal distribution .
function: def random_normal_variable(shape, mean, scale, dtype=None, name=None,
                             seed=None):
    """Instantiates a variable with values drawn from a normal distribution.
  
    Arguments:
        shape: Tuple of integers, shape of returned Keras variable.
        mean: Float, mean of the normal distribution.
        scale: Float, standard deviation of the normal distribution.
        dtype: String, dtype of returned Keras variable.
        name: String, name of returned Keras variable.
        seed: Integer, random seed.
  
    Returns:
        A Keras variable, filled with drawn samples.
  
    Example:
    ```python
        # TensorFlow example
        >>> kvar = K.random_normal_variable((2,3), 0, 1)
        >>> kvar
        <tensorflow.python.ops.variables.Variable object at 0x10ab12dd0>
        >>> K.eval(kvar)
        array([[ 1.19591331,  0.68685907, -0.63814116],
               [ 0.92629528,  0.28055015,  1.70484698]], dtype=float32)
    ```
    """
    if dtype is None:
      dtype = floatx()
    tf_dtype = _convert_string_dtype(dtype)
    if seed is None:
      # ensure that randomness is conditioned by the Numpy RNG
      seed = np.random.randint(10e8)
    value = init_ops.random_normal_initializer(
        mean, scale, dtype=tf_dtype, seed=seed)(shape)
    return variable(value, dtype=dtype, name=name)
function_tokens: def random_normal_variable ( shape , mean , scale , dtype = None , name = None , seed = None ) : if dtype is None : dtype = floatx ( ) tf_dtype = _convert_string_dtype ( dtype ) if seed is None : # ensure that randomness is conditioned by the Numpy RNG seed = np . random . randint ( 10e8 ) value = init_ops . random_normal_initializer ( mean , scale , dtype = tf_dtype , seed = seed ) ( shape ) return variable ( value , dtype = dtype , name = name )