Add list like stars to see if that improves the formatting on the website.

PiperOrigin-RevId: 297442449

Author: yashk2810

tensorflow_estimator/python/estimator/estimator.py

@@ -96,6 +96,21 @@ class Estimator(object):
   constructor enforces this). Subclasses should use `model_fn` to configure
   the base class, and may add methods implementing specialized functionality.
 
+  See [estimators](https://tensorflow.org/guide/estimators) for more
+    information.
+
+  To warm-start an `Estimator`:
+
+  ```python
+  estimator = tf.estimator.DNNClassifier(
+      feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb],
+      hidden_units=[1024, 512, 256],
+      warm_start_from="/path/to/checkpoint/dir")
+  ```
+
+  For more details on warm-start configuration, see
+  `tf.estimator.WarmStartSettings`.
+
   @compatibility(eager)
   Calling methods of `Estimator` will work while eager execution is enabled.
   However, the `model_fn` and `input_fn` is not executed eagerly, `Estimator`
@@ -114,42 +129,29 @@ def __init__(self,
                warm_start_from=None):
     """Constructs an `Estimator` instance.
 
-    See [estimators](https://tensorflow.org/guide/estimators) for more
-    information.
-
-    To warm-start an `Estimator`:
-
-    ```python
-    estimator = tf.estimator.DNNClassifier(
-        feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb],
-        hidden_units=[1024, 512, 256],
-        warm_start_from="/path/to/checkpoint/dir")
-    ```
 
-    For more details on warm-start configuration, see
-    `tf.estimator.WarmStartSettings`.
 
     Args:
       model_fn: Model function. Follows the signature:
-        `features` -- This is the first item returned from the `input_fn`
+        * `features` -- This is the first item returned from the `input_fn`
         passed to `train`, `evaluate`, and `predict`. This should be a
         single `tf.Tensor` or `dict` of same.
-        `labels` -- This is the second item returned from the `input_fn`
+        * `labels` -- This is the second item returned from the `input_fn`
         passed to `train`, `evaluate`, and `predict`. This should be a
         single `tf.Tensor` or `dict` of same (for multi-head models). If
         mode is `tf.estimator.ModeKeys.PREDICT`, `labels=None` will be
         passed. If the `model_fn`'s signature does not accept `mode`, the
         `model_fn` must still be able to handle `labels=None`.
-        `mode` -- Optional. Specifies if this is training, evaluation or
+        * `mode` -- Optional. Specifies if this is training, evaluation or
         prediction. See `tf.estimator.ModeKeys`.
         `params` -- Optional `dict` of hyperparameters.  Will receive what is
         passed to Estimator in `params` parameter. This allows to configure
         Estimators from hyper parameter tuning.
-        `config` -- Optional `estimator.RunConfig` object. Will receive what
+        * `config` -- Optional `estimator.RunConfig` object. Will receive what
         is passed to Estimator as its `config` parameter, or a default
         value. Allows setting up things in your `model_fn` based on
         configuration such as `num_ps_replicas`, or `model_dir`.
-        Returns -- `tf.estimator.EstimatorSpec`
+        * Returns -- `tf.estimator.EstimatorSpec`
       model_dir: Directory to save model parameters, graph and etc. This can
         also be used to load checkpoints from the directory into an estimator to
         continue training a previously saved model. If `PathLike` object, the
@@ -560,12 +562,12 @@ def predict(self,
         https://tensorflow.org/guide/premade_estimators#create_input_functions)
         for more information. The function should construct and return one of
         the following:
-          `tf.data.Dataset` object -- Outputs of `Dataset` object must have
-            same constraints as below.
-          features -- A `tf.Tensor` or a dictionary of string feature name to
-            `Tensor`. features are consumed by `model_fn`. They should satisfy
-            the expectation of `model_fn` from inputs. * A tuple, in which case
-            the first item is extracted as features.
+        * `tf.data.Dataset` object -- Outputs of `Dataset` object must have
+          same constraints as below.
+        * features -- A `tf.Tensor` or a dictionary of string feature name to
+          `Tensor`. features are consumed by `model_fn`. They should satisfy
+          the expectation of `model_fn` from inputs. * A tuple, in which case
+          the first item is extracted as features.
       predict_keys: list of `str`, name of the keys to predict. It is used if
         the `tf.estimator.EstimatorSpec.predictions` is a `dict`. If
         `predict_keys` is used then rest of the predictions will be filtered