tfjs: Error: graph model model.predict throws `avoid the dynamic ops` error

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System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow.js): No
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Pop-os 20.10
• Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: Any android device (checked on LG G8x, Oneplus 3)
• TensorFlow.js installed from (npm or script link): npm
• TensorFlow.js version (use command below): ^2.7.0
• Browser version: NA
• Tensorflow.js Converter Version: 2.3.0

Describe the current behavior I used tensorflow-js converter to obtain a graph model from keras saved-model. The conversion is successful and also throw any error when loaded using tf.loadGraphModel but when trying to call model.predict it throws following error

Error: This execution contains the node 'StatefulPartitionedCall/model/rnn_1/while/exit/_65', 
which has the dynamic op 'Exit'. Please use model.executeAsync() instead. 
Alternatively, to avoid the dynamic ops, specify the inputs [Identity]

When I try model.executeAsync(), following error occurs

Error: Cannot compute the outputs [Identity] from the provided inputs [the_input]. 
Consider providing the following inputs: []. Alternatively, to avoid the dynamic ops, 
use model.execute() and specify the inputs [Identity]

For model.execute() similar kind of error is thrown. I also tried to specify the outputs arguments for execute and executeAsync but nothing seems to work.

My model consists of following layers:

• InputLayer
• Conv1D
• BatchNormalisation
• GRU
• TimeDistributed
• Softmax - Activation

The tensorflow-js converter readme page mentions that inference is faster for graphModel which I intend to leverage.

Describe the expected behavior model.predict should return a Tensor/TensorArray.