GRU() got an unexpected keyword argument 'input_shape'

GRU is a type of recurrent neural network (RNN) layer commonly used in deep learning models for sequence data. The GRU layer does not have an `input_shape` argument. Instead, you need to specify the input shape when defining the input layer of your model. Here is an example of how you can define a GRU layer in a model:

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.GRU(units=64, input_shape=(sequence_length, input_dim)),
    # Additional layers or output layer here
])

In the above code, `sequence_length` represents the length of the input sequences, and `input_dim` represents the dimensionality of each input element. Make sure to replace `sequence_length` and `input_dim` with the appropriate values for your specific problem.