code: def diff(x, order=1, exclusive=False, axis=-1, dtype=None, name=None):
      """Computes the difference between elements of an array at a regular interval.
  
      For a difference along the final axis, if exclusive is True, then computes:
  
      ```
        result[..., i] = x[..., i+order] - x[..., i] for i < size(x) - order
  
      ```
  
      This is the same as doing `x[..., order:] - x[..., :-order]`. Note that in
      this case the result `Tensor` is smaller in size than the input `Tensor`.
  
      If exclusive is False, then computes:
  
      ```
        result[..., i] = x[..., i] - x[..., i-order] for i >= order
        result[..., i] = x[..., i]  for 0 <= i < order
  
      ```
  
      #### Example
  
      ```python
        x = tf.constant([1, 2, 3, 4, 5])
        dx = diff(x, order=1, exclusive=False)  # Returns [1, 1, 1, 1, 1]
        dx1 = diff(x, order=1, exclusive=True)  # Returns [1, 1, 1, 1]
        dx2 = diff(x, order=2, exclusive=False)  # Returns [1, 2, 2, 2, 2]
      ```
  
      Args:
        x: A `Tensor` of shape `batch_shape + [n]` and of any dtype for which
          arithmetic operations are permitted.
        order: Positive Python int. The order of the difference to compute. `order =
          1` corresponds to the difference between successive elements.
          Default value: 1
        exclusive: Python bool. See description above.
          Default value: False
        axis: Python int. The axis of `x` along which to difference.
          Default value: -1 (the final axis).
        dtype: Optional `tf.DType`. If supplied, the dtype for `x` to use when
          converting to `Tensor`.
          Default value: None which maps to the default dtype inferred by TF.
        name: Python `str` name prefixed to Ops created by this class.
          Default value: None which is mapped to the default name 'diff'.
  
      Returns:
        diffs: A `Tensor` of the same dtype as `x`. If `exclusive` is True,
          then the shape is `batch_shape + [n-min(order, n)]`, otherwise it is
          `batch_shape + [n]`. The final dimension of which contains the differences
          of the requested order.
      """
      with tf.name_scope(name or "diff"):
          x = tf.convert_to_tensor(x, dtype=dtype)
  
          slices = x.shape.rank * [slice(None)]
          slices[axis] = slice(None, -order)
          x0 = x[slices]
          slices[axis] = slice(order, None)
          x1 = x[slices]
          exclusive_diff = x1 - x0
  
          if exclusive:
              return exclusive_diff
  
          slices[axis] = slice(None, order)
          return tf.concat([x[slices], exclusive_diff], axis=axis)
  
label_desc: Use image node_id 1 for calling a global function with example usage: diff(x, order, exclusive, axis, dtype, name) and returns: tf, exclusive_diff