tensorflow_probability/python/internal/distribution_util.py
def get_broadcast_shape(*tensors):
  """Get broadcast shape as a Python list of integers (preferred) or `Tensor`.

  Args:
    *tensors:  One or more `Tensor` objects (already converted!).

  Returns:
    broadcast shape:  Python list (if shapes determined statically), otherwise
      an `int32` `Tensor`.
  """
  # Try static.
  s_shape = tensors[0].shape
  for t in tensors[1:]:
    s_shape = tf.broadcast_static_shape(s_shape, t.shape)
  if tensorshape_util.is_fully_defined(s_shape):
    return tensorshape_util.as_list(s_shape)

  # Fallback on dynamic.
  d_shape = tf.shape(input=tensors[0])
  for t in tensors[1:]:
    d_shape = tf.broadcast_dynamic_shape(d_shape, tf.shape(input=t))
  return d_shape
def get_broadcast_shape(*tensors):
  """Get broadcast shape as a Python list of integers (preferred) or `Tensor`.

  Args:
    *tensors:  One or more `Tensor` objects (already converted!).

  Returns:
    broadcast shape:  Python list (if shapes determined statically), otherwise
      an `int32` `Tensor`.
  """
  # Try static.
  s_shape = tensors[0].shape
  for t in tensors[1:]:
    s_shape = tf.broadcast_static_shape(s_shape, t.shape)
  if tensorshape_util.is_fully_defined(s_shape):
    return tensorshape_util.as_list(s_shape)

  # Fallback on dynamic.
  d_shape = tf.shape(input=tensors[0])
  for t in tensors[1:]:
    d_shape = tf.broadcast_dynamic_shape(d_shape, tf.shape(input=t))
  return d_shape
Get broadcast shape as a Python list of integers (preferred) or `Tensor`.

  Args:
    *tensors:  One or more `Tensor` objects (already converted!).

  Returns:
    broadcast shape:  Python list (if shapes determined statically), otherwise
      an `int32` `Tensor`.