message: Minor fixes in docstring of compile method
  Consistent usage of backquotes
  minor change in wording
  misspelling ie. > i.e.
diff: @@ -501,12 +501,13 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  categorical crossentropy 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;
  + used and reduction is set to `None`, return value has the shape
  + `[batch_size, d0, .. dN-1]` i.e. 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.
  + then be the sum of all individual losses, unless `loss_weights` is
  + specified.
  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
  @@ -515,9 +516,9 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  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']}`.
  - You can also pass a list (len = len(outputs)) of lists of metrics
  - such as `metrics=[['accuracy'], ['accuracy', 'mse']]` or
  - `metrics=['accuracy', ['accuracy', 'mse']]`. When you pass the
  + You can also pass a list to specify a metric or a list of metrics
  + for each output, such as `metrics=[['accuracy'], ['accuracy', 'mse']]`
  + or `metrics=['accuracy', ['accuracy', 'mse']]`. When you pass the
  strings 'accuracy' or 'acc', we convert this to one of
  `tf.keras.metrics.BinaryAccuracy`,
  `tf.keras.metrics.CategoricalAccuracy`,
  @@ -533,7 +534,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  outputs. If a dict, it is expected to map output names (strings)
  to scalar coefficients.
  weighted_metrics: List of metrics to be evaluated and weighted by
  - sample_weight or class_weight during training and testing.
  + `sample_weight` or `class_weight` during training and testing.
  run_eagerly: Bool. Defaults to `False`. If `True`, this `Model`'s
  logic will not be wrapped in a `tf.function`. Recommended to leave
  this as `None` unless your `Model` cannot be run inside a
  