def visualize_qualitative_analysis(inputs, model, samples=<NUM_LIT:1>, batch_size=<NUM_LIT:3>,<EOL>length=<NUM_LIT:8>):
average = lambda dist: tf.reduce_mean(<EOL>input_tensor=dist.mean(), axis=<NUM_LIT:0>)  <EOL>with tf.compat.v1.name_scope("<STR_LIT>"):<EOL><INDENT>reconstruct = functools.partial(model.reconstruct, inputs=inputs,<EOL>samples=samples)<EOL>visualize_reconstruction(inputs, average(reconstruct()))<EOL>visualize_reconstruction(inputs, average(reconstruct(sample_static=True)),<EOL>name="<STR_LIT>")<EOL>visualize_reconstruction(inputs, average(reconstruct(sample_dynamic=True)),<EOL>name="<STR_LIT>")<EOL>visualize_reconstruction(inputs, average(reconstruct(swap_static=True)),<EOL>name="<STR_LIT>")<EOL>visualize_reconstruction(inputs, average(reconstruct(swap_dynamic=True)),<EOL>name="<STR_LIT>")<EOL><DEDENT>with tf.compat.v1.name_scope("<STR_LIT>"):<EOL><INDENT>generate = functools.partial(model.generate, batch_size=batch_size,<EOL>length=length, samples=samples)<EOL>image_summary(average(generate(fix_static=True)), "<STR_LIT>")<EOL>image_summary(average(generate(fix_dynamic=True)), "<STR_LIT>")<EOL><DEDENT>
Visualizes a qualitative analysis of a given model.

    Args:
      inputs: A tensor of the original inputs, of shape [batch, timesteps,
        h, w, c].
      model: A DisentangledSequentialVAE model.
      samples: Number of samples to draw from the latent distributions.
      batch_size: Number of sequences to generate.
      length: Number of timesteps to generate for each sequence.