Correct description typos.

PiperOrigin-RevId: 372738818

official/modeling/optimization/configs/learning_rate_config.py

@@ -154,11 +154,11 @@ class PowerAndLinearDecayLrConfig(base_config.Config):
   1) offset_step < 0, the actual learning rate equals initial_learning_rate.
   2) offset_step <= total_decay_steps * (1 - linear_decay_fraction), the
   actual learning rate equals lr * offset_step^power.
-  3) total_decay_steps * (1 - linear_decay_fraction) < offset_step <
+  3) total_decay_steps * (1 - linear_decay_fraction) <= offset_step <
   total_decay_steps, the actual learning rate equals lr * offset_step^power *
   (total_decay_steps - offset_step) / (total_decay_steps *
   linear_decay_fraction).
-  4) offset_step > total_decay_steps, the actual learning rate equals zero.
+  4) offset_step >= total_decay_steps, the actual learning rate equals zero.
 
   Attributes:
     name: The name of the learning rate schedule. Defaults to

official/modeling/optimization/configs/optimization_config.py

@@ -57,7 +57,7 @@ class LrConfig(oneof.OneOfConfig):
   """Configuration for lr schedule.
 
   Attributes:
-    type: 'str', type of lr schedule to be used, on the of fields below.
+    type: 'str', type of lr schedule to be used, one of the fields below.
     constant: constant learning rate config.
     stepwise: stepwise learning rate config.
     exponential: exponential learning rate config.
@@ -86,7 +86,7 @@ class WarmupConfig(oneof.OneOfConfig):
   """Configuration for lr schedule.
 
   Attributes:
-    type: 'str', type of warmup schedule to be used, on the of fields below.
+    type: 'str', type of warmup schedule to be used, one of the fields below.
     linear: linear warmup config.
     polynomial: polynomial warmup config.
   """

official/modeling/optimization/lr_schedule.py

@@ -205,11 +205,11 @@ class PowerAndLinearDecay(tf.keras.optimizers.schedules.LearningRateSchedule):
   1) offset_step < 0, the actual learning rate equals initial_learning_rate.
   2) offset_step <= total_decay_steps * (1 - linear_decay_fraction), the
   actual learning rate equals lr * offset_step^power.
-  3) total_decay_steps * (1 - linear_decay_fraction) < offset_step <
+  3) total_decay_steps * (1 - linear_decay_fraction) <= offset_step <
   total_decay_steps, the actual learning rate equals lr * offset_step^power *
   (total_decay_steps - offset_step) / (total_decay_steps *
   linear_decay_fraction).
-  4) offset_step > total_decay_steps, the actual learning rate equals zero.
+  4) offset_step >= total_decay_steps, the actual learning rate equals zero.
   """
 
   def __init__(self,