Move some ndlstm functions to contrib (#16816)

* 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.

* Modified implementation of ndlstm_base_dynamic.

It now uses a BasicLSTMCell that has state_is_tuple=True to address deprecation. Right now it is still unknown why it was set to false in the first place.

* Imported lstm1d and lstm2d in ndlstm __init__.py.

Makes importing ndlstm modules easier.

* Added testGetBlocks in lstm2d_test.

* Removed testGetBlocks.py

* Modified lstm1d.ndlstm_base_unrolled to use lstm_cell with state_is_tuple = True.

* Copied some lstm2d.py functions in ndlstm module to contrib.layers.

* Update lstm1d.py

Reverted changes made.

* Update layers_test.py

Fixed failing test.

* Modified layers.py and layers_test.py.

Made them pass pylint tests.