code: def get_dummy_decoder_context(
      converter,
      batch=2,
      d_model=6,
      length=4,
      mode="incremental",
      initial_position=None,
      state=None,
      inputs=None,
  ):
      batch_dim = mtf.Dimension("batch", batch)
      length_dim = mtf.Dimension("length", length)
  
      # Set up a dummy model
      layer_stack = transformer.LayerStack(layers=[])
      model = transformer.Unitransformer(
          d_model=d_model,
          input_vocab_size=10,  # dummy values
          output_vocab_size=10,  # dummy values
          autoregressive=True,
          max_length=length,
          layer_stack=layer_stack,
      )
  
      if state is not None:
          state_mtf = converter.convert_np_array_to_mtf_tensor(
              state,
              dtype=tf.float32,
              dim_names=["batch", "length", "d_model"],
          )
          states = [state_mtf]
      else:
          states = None
  
      if initial_position:
          initial_position = mtf.constant(
              converter.mesh,
              initial_position,
              shape=mtf.Shape([batch_dim]),
              dtype=tf.int32,
          )
  
      if inputs is not None:
          inputs = converter.convert_np_array_to_mtf_tensor(
              inputs, dim_names=["batch", "length"]
          )
  
      context = transformer.Context(
          model=model,
          mode=mode,
          states=states,
          new_states=[],
          mesh=converter.mesh,
          batch_dims=[batch_dim],
          length_dim=length_dim,
          variable_dtype=mtf.VariableDType(tf.float32),
          sequence_id=1,
          inputs=inputs,
          initial_position=initial_position,
      )
      return context
  
imports: import collections
  import mesh_tensorflow
  from mesh_tensorflow import test_utils
  from mesh_tensorflow.transformer import transformer
  from mesh_tensorflow.transformer import transformer_layers
  import mock
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: get_dummy_decoder_context(converter, batch, d_model, length, mode, initial_position, state, inputs) and returns: context