@staticmethod<EOL><INDENT>def new(params, event_shape=(), validate_args=False, name=None):<DEDENT>
with tf.compat.v1.name_scope(name, '<STR_LIT>',<EOL>[params, event_shape]):<EOL><INDENT>params = tf.convert_to_tensor(value=params, name='<STR_LIT>')<EOL>event_shape = dist_util.expand_to_vector(<EOL>tf.convert_to_tensor(<EOL>value=event_shape, name='<STR_LIT>', dtype_hint=tf.int32),<EOL>tensor_name='<STR_LIT>')<EOL>output_shape = tf.concat([<EOL>tf.shape(input=params)[:-<NUM_LIT:1>],<EOL>event_shape,<EOL>],<EOL>axis=<NUM_LIT:0>)<EOL>loc_params, scale_params = tf.split(params, <NUM_LIT:2>, axis=-<NUM_LIT:1>)<EOL>return tfd.Independent(<EOL>tfd.Logistic(<EOL>loc=tf.reshape(loc_params, output_shape),<EOL>scale=tf.math.softplus(tf.reshape(scale_params, output_shape)),<EOL>validate_args=validate_args),<EOL>reinterpreted_batch_ndims=tf.size(input=event_shape),<EOL>validate_args=validate_args)<EOL><DEDENT>
Create the distribution instance from a `params` vector.