There cannot be newlines in Args, Returns, Raises section, because in markdown that's a paragraph and the website render comes out wrong. See https://www.tensorflow.org/api_docs/python/tf/keras/Sequential?version=nightly#arguments_3

PiperOrigin-RevId: 291990085
Change-Id: Ic698330b7939c3847fca0d6dbfe4a610569b873c

tensorflow/python/keras/engine/training.py

@@ -236,7 +236,6 @@ class Model(network.Network, version_utils.VersionSelector):
             See `tf.keras.optimizers`.
         loss: String (name of objective function), objective function or
             `tf.keras.losses.Loss` instance. See `tf.keras.losses`.
-
             An objective function is any callable with the signature
             `loss = fn(y_true, y_pred)`, where
             y_true = ground truth values with shape = `[batch_size, d0, .. dN]`,
@@ -244,23 +243,19 @@ class Model(network.Network, version_utils.VersionSelector):
             where shape = `[batch_size, d0, .. dN-1]`.
             y_pred = predicted values with shape = `[batch_size, d0, .. dN]`.
             It returns a weighted loss float tensor.
-
             If a custom `Loss` instance is used and reduction is set to NONE,
             return value has the shape [batch_size, d0, .. dN-1] ie. per-sample
             or per-timestep loss values; otherwise, it is a scalar.
-
             If the model has multiple outputs, you can use a different loss on
             each output by passing a dictionary or a list of losses. The loss
             value that will be minimized by the model will then be the sum of
             all individual losses.
         metrics: List of metrics to be evaluated by the model during training
             and testing.
-
             Each of this can be a string (name of a built-in function), function
             or a `tf.keras.metrics.Metric` instance. See `tf.keras.metrics`.
             Typically you will use `metrics=['accuracy']`. A function is any
             callable with the signature `result = fn(y_true, y_pred)`.
-
             To specify different metrics for different outputs of a
             multi-output model, you could also pass a dictionary, such as
             `metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}`.