Analysis of the output from tf.nn.dynamic_rnn tensorflow function

I am not able to understand the output from tf.nn.dynamic_rnn tensorflow function. The document just tells about the size of the output, but it doesn't tell what does each row/column means. From the documentation:

outputs: The RNN output Tensor.

If time_major == False (default), this will be a Tensor shaped: [batch_size, max_time, cell.output_size].

If time_major == True, this will be a Tensor shaped: [max_time, batch_size, cell.output_size].

Note, if cell.output_size is a (possibly nested) tuple of integers or TensorShape objects, then outputs will be a tuple having the
same structure as cell.output_size, containing Tensors having shapes corresponding to the shape data in cell.output_size.

state: The final state. If cell.state_size is an int, this will be shaped [batch_size, cell.state_size]. If it is a
TensorShape, this will be shaped [batch_size] + cell.state_size.
If it is a (possibly nested) tuple of ints or TensorShape, this will be a tuple having the corresponding shapes.

The outputs tensor is a 3-D matrix but what does each row/column represent?