message: Narrow down argmin/argmax contract to always return the smallest index for ties
  Currently we get this behavior consistently across TF/XLA:CPU/XLA:GPU/XLA:TPU, and it also matches Numpy semantics.
diff: @@ -256,7 +256,7 @@ def argmax(input,
  def argmax_v2(input, axis=None, output_type=dtypes.int64, name=None):
  """Returns the index with the largest value across axes of a tensor.
  - Note that in case of ties the identity of the return value is not guaranteed.
  + In case of identity returns the smallest index.
  For example:
  @@ -269,6 +269,9 @@ def argmax_v2(input, axis=None, output_type=dtypes.int64, name=None):
  <tf.Tensor: shape=(5,), dtype=int64, numpy=array([2, 2, 0, 2, 2])>
  >>> tf.math.argmax(B, 1)
  <tf.Tensor: shape=(3,), dtype=int64, numpy=array([2, 2, 1])>
  + >>> C = tf.constant([0, 0, 0, 0])
  + >>> tf.math.argmax(C) # Returns smallest index in case of ties
  + <tf.Tensor: shape=(), dtype=int64, numpy=0>
  Args:
  input: A `Tensor`.
  @@ -307,7 +310,7 @@ def argmin(input,
  def argmin_v2(input, axis=None, output_type=dtypes.int64, name=None):
  """Returns the index with the smallest value across axes of a tensor.
  - Note that in case of ties the identity of the return value is not guaranteed.
  + Returns the smallest index in case of ties.
  Args:
  input: A `Tensor`. Must be one of the following types: `float32`, `float64`,
  