docstr: Creates `data`, `sparse`, `sizes` tensors from shapes list.
code: def _create_default_tensors_from_shapes(self, shapes):
      """Creates `data`, `sparse`, `sizes` tensors from shapes list."""
      data = tf.convert_to_tensor(
          value=np.random.uniform(size=shapes[0]).astype(np.float32)
      )
      sparse = _dense_to_sparse(
          np.ones(shape=shapes[1], dtype=np.float32)
      )
      if shapes[2] is not None:
          sizes = tf.convert_to_tensor(
              value=np.ones(shape=shapes[2], dtype=np.int32)
          )
      else:
          sizes = None
      return data, sparse, sizes
  
imports: from absl.testing import parameterized
  import numpy
  from scipy import linalg
  import tensorflow
  from tensorflow_graphics.geometry.convolution import utils
  from tensorflow_graphics.util import test_case
label_desc: Use image node_id 1 for calling the UtilsCheckValidGraphConvolutionInputTests obj's underlying member method code with example usage: obj._create_default_tensors_from_shapes(shapes) and returns: data,  sparse,  sizes