code: def _trainable_linear_operator_full_matrix(
      shape, scale_initializer=1e-2, dtype=None, name=None
  ):
      """Build a trainable `LinearOperatorFullMatrix` instance.
  
      Args:
        shape: Shape of the `LinearOperator`, equal to `[b0, ..., bn, h, w]`, where
          `b0...bn` are batch dimensions `h` and `w` are the height and width of the
          matrix represented by the `LinearOperator`.
        scale_initializer: Variables are initialized with samples from
          `Normal(0, scale_initializer)`.
        dtype: `tf.dtype` of the `LinearOperator`.
        name: str, name for `tf.name_scope`.
      Yields:
        *parameters: sequence of `trainable_state_util.Parameter` namedtuples.
          These are intended to be consumed by
          `trainable_state_util.as_stateful_builder` and
          `trainable_state_util.as_stateless_builder` to define stateful and
          stateless variants respectively.
      """
      with tf.name_scope(
          name or "trainable_linear_operator_full_matrix"
      ):
          if dtype is None:
              dtype = dtype_util.common_dtype(
                  [scale_initializer], dtype_hint=tf.float32
              )
          scale_initializer = tf.convert_to_tensor(
              scale_initializer, dtype
          )
          scale_matrix = yield trainable_state_util.Parameter(
              init_fn=functools.partial(
                  samplers.normal,
                  mean=0.0,
                  stddev=scale_initializer,
                  shape=shape,
                  dtype=dtype,
              ),
              name="scale_matrix",
          )
          return tf.linalg.LinearOperatorFullMatrix(matrix=scale_matrix)
  
imports: import functools
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import fill_scale_tril
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import trainable_state_util
  from tensorflow.python.util import nest
label_desc: Use image node_id 4 for calling a global function with example usage: _trainable_linear_operator_full_matrix(shape, scale_initializer, dtype, name) and returns: tf