Created dense_to_sparse in contrib.layers (#16119)

* Added ctc_loss_dense_labels. This does the conversion of dense labels into sparse ones to be passed into the core ctc_loss function.

* Removed constant_op from the import.

* Matched ctc_loss_dense_labels with the other layers ops.

* Added ctc_loss_dense_labels to contrib.layers __init__.py file

* Added missing comma to list of ops.

* Reordred arguments for ctc_loss_dense_labels

Labels should be first then inputs for ctc_loss.

* Removed ctc_loss_dense_labels.

Replaced it with dense_to_sparse instead so that there'll be only one ctc_loss function.

* Replaced ctc_loss_dense_labels with dense_to_sparse

* Fixed dense_to_sparse. Some of the names of the variables did not match with that of the parameters.

* Updated documentation for dense_to_sparse since it can accept a tensor of any shape.

* Added test case for dense_to_sparse.

* Updated documentation. Dense to sparse accepts int tensors.

* Fixed testDenseFromConstantToSparse.

The sparse_to_dense order of arguments in the test are wrong and the expected constant should be of int64.