message: Improve formatting of shapes in tf.losses documentation
diff: @@ -377,7 +377,7 @@ def huber_loss(labels, predictions, weights=1.0, delta=1.0, scope=None,
  `weights` acts as a coefficient for the loss. If a scalar is provided, then
  the loss is simply scaled by the given value. If `weights` is a tensor of size
  - [batch_size], then the total loss for each sample of the batch is rescaled
  + `[batch_size]`, then the total loss for each sample of the batch is rescaled
  by the corresponding element in the `weights` vector. If the shape of
  `weights` matches the shape of `predictions`, then the loss of each
  measurable element of `predictions` is scaled by the corresponding value of
  @@ -435,7 +435,7 @@ def log_loss(labels, predictions, weights=1.0, epsilon=1e-7, scope=None,
  `weights` acts as a coefficient for the loss. If a scalar is provided, then
  the loss is simply scaled by the given value. If `weights` is a tensor of size
  - [batch_size], then the total loss for each sample of the batch is rescaled
  + `[batch_size]`, then the total loss for each sample of the batch is rescaled
  by the corresponding element in the `weights` vector. If the shape of
  `weights` matches the shape of `predictions`, then the loss of each
  measurable element of `predictions` is scaled by the corresponding value of
  @@ -502,7 +502,7 @@ def mean_pairwise_squared_error(
  `weights` acts as a coefficient for the loss. If a scalar is provided, then
  the loss is simply scaled by the given value. If `weights` is a tensor of size
  - [batch_size], then the total loss for each sample of the batch is rescaled
  + `[batch_size]`, then the total loss for each sample of the batch is rescaled
  by the corresponding element in the `weights` vector.
  Args:
  @@ -576,7 +576,7 @@ def mean_squared_error(
  `weights` acts as a coefficient for the loss. If a scalar is provided, then
  the loss is simply scaled by the given value. If `weights` is a tensor of size
  - [batch_size], then the total loss for each sample of the batch is rescaled
  + `[batch_size]`, then the total loss for each sample of the batch is rescaled
  by the corresponding element in the `weights` vector. If the shape of
  `weights` matches the shape of `predictions`, then the loss of each
  measurable element of `predictions` is scaled by the corresponding value of
  @@ -796,7 +796,7 @@ def sparse_softmax_cross_entropy(
  `weights` acts as a coefficient for the loss. If a scalar is provided,
  then the loss is simply scaled by the given value. If `weights` is a
  - tensor of shape [`batch_size`], then the loss weights apply to each
  + tensor of shape `[batch_size]`, then the loss weights apply to each
  corresponding sample.
  Args:
  