diff --git a/tensorflow/lite/python/lite.py b/tensorflow/lite/python/lite.py
index 4c506dca229..1b226b7d25b 100644
--- a/tensorflow/lite/python/lite.py
+++ b/tensorflow/lite/python/lite.py
@@ -365,7 +365,9 @@ class TFLiteConverterV2(TFLiteConverterBase):
       to apply when converting the model. E.g. `[Optimize.DEFAULT]`
     representative_dataset: A representative dataset that can be used to
       generate input and output samples for the model. The converter can use the
-      dataset to evaluate different optimizations.
+      dataset to evaluate different optimizations. Note that this is an optional
+      attribute but it is necessary if INT8 is the only support builtin ops in
+      target ops.
     experimental_new_converter: Experimental flag, subject to change.
       Enables MLIR-based conversion instead of TOCO conversion.
     experimental_new_quantizer: Experimental flag, subject to change.
diff --git a/tensorflow/python/keras/optimizer_v2/nadam.py b/tensorflow/python/keras/optimizer_v2/nadam.py
index 2b94b2267e3..a572e225b62 100644
--- a/tensorflow/python/keras/optimizer_v2/nadam.py
+++ b/tensorflow/python/keras/optimizer_v2/nadam.py
@@ -80,7 +80,7 @@ class Nadam(optimizer_v2.OptimizerV2):
         rate for the exponentially weighted infinity norm.
       epsilon: A small constant for numerical stability.
       name: Optional name for the operations created when applying gradients.
-        Defaults to "Adamax".
+        Defaults to "Nadam".
       **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`,
         `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip
         gradients by value, `decay` is included for backward compatibility to