The current format is messing up the formatting on the site. Let's see it this works.

PiperOrigin-RevId: 297149577

Author: yashk2810

tensorflow_estimator/python/estimator/estimator.py

@@ -131,26 +131,25 @@ def __init__(self,
 
     Args:
       model_fn: Model function. Follows the signature:
-        * Args:
-          * `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`
-            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
-            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
-            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`
+        `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`
+        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
+        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
+        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`
       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
@@ -559,11 +558,11 @@ def predict(self,
         (`tf.errors.OutOfRangeError` or `StopIteration`). See [Premade
         Estimators](
         https://tensorflow.org/guide/premade_estimators#create_input_functions)
-          for more information. The function should construct and return one of
+        for more information. The function should construct and return one of
         the following:
-          * A `tf.data.Dataset` object: Outputs of `Dataset` object must have
+          `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
+          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.