message: updated zeros and zeros_like
  Corrected typo's, formatted, and add examples
diff: @@ -1212,24 +1212,30 @@ def zeros(shape, dtype=None, name=None):
  """Instantiates an all-zeros variable and returns it.
  Arguments:
  - shape: Tuple of integers, shape of returned Keras variable
  - dtype: String, data type of returned Keras variable
  - name: String, name of returned Keras variable
  + shape: Tuple or list of integers, shape of returned Keras variable
  + dtype: data type of returned Keras variable
  + name : name of returned Keras variable
  Returns:
  A variable (including Keras metadata), filled with `0.0`.
  Note that if `shape` was symbolic, we cannot return a variable,
  and will return a dynamically-shaped tensor instead.
  - Example:
  + For Example:
  +
  ```python
  - >>> from keras import backend as K
  - >>> kvar = K.zeros((3,4))
  - >>> K.eval(kvar)
  + from tensorflow.keras import backend as K
  + kvar = K.zeros((3,4))
  + K.eval(kvar)
  array([[ 0., 0., 0., 0.],
  [ 0., 0., 0., 0.],
  [ 0., 0., 0., 0.]], dtype=float32)
  + A = tf.constant([1,2,3])
  + kvar2 = K.zeros(A.shape) # [0., 0., 0.] float32 by default
  + kvar3 = K.zeros(A.shape,dtype=tf.int32) # [0, 0, 0] with int32 dtype
  + kvar4 = K.zeros([2,3]) # [[0., 0., 0.], [0., 0., 0.]]
  ```
  +
  """
  with ops.init_scope():
  if dtype is None:
  @@ -1312,22 +1318,24 @@ def zeros_like(x, dtype=None, name=None):
  Arguments:
  x : Keras variable or Keras tensor.
  - dtype: String, dtype of returned Keras variable.
  - None uses the dtype of x.
  - name: String, name for the variable to create.
  + dtype: dtype of returned Keras variable.
  + None uses the dtype of `x`.
  + name : name for the variable to create.
  Returns:
  - A Keras variable with the shape of x filled with zeros.
  + A Keras variable with the shape of `x` filled with zeros.
  +
  + For Example:
  - Example:
  ```python
  - >>> from keras import backend as K
  - >>> kvar = K.variable(np.random.random((2,3)))
  - >>> kvar_zeros = K.zeros_like(kvar)
  - >>> K.eval(kvar_zeros)
  - array([[ 0., 0., 0.],
  - [ 0., 0., 0.]], dtype=float32)
  + from tensorflow.keras import backend as K
  + kvar = K.variable(np.random.random((2,3)))
  + kvar_zeros = K.zeros_like(kvar)
  + K.eval(kvar_zeros)
  + # array([[ 0., 0., 0.],
  + # [ 0., 0., 0.]], dtype=float32)
  ```
  +
  """
  return array_ops.zeros_like(x, dtype=dtype, name=name)
  