def range_dimension(self):
"""Dimension (in the sense of vector spaces) of the range of this operator.
If this operator acts like the batch matrix `A` with
`tensor_shape.TensorShape(A.shape) = [B1,...,Bb, M, N]`, then this returns `M`.
Returns:
`Dimension` object.
"""
# Derived classes get this "for free" once .shape is implemented.
if tensor_shape.TensorShape(self.shape).dims:
return tensor_shape.TensorShape(self.shape).dims[-2]
else:
return tensor_shape.Dimension(None)
def _check_shapes(self):
"""Static check that shapes are compatible."""
# Broadcast shape also checks that u and v are compatible.
uv_shape = _ops.broadcast_static_shape(
tensor_shape.TensorShape(self.u.shape), tensor_shape.TensorShape(self.v.shape))
batch_shape = _ops.broadcast_static_shape(
self.base_operator.batch_shape, uv_shape[:-2])
tensor_shape.Dimension(
self.base_operator.domain_dimension).assert_is_compatible_with(
uv_shape[-2])
if self._diag_update is not None:
tensor_shape.dimension_at_index(uv_shape, -1).assert_is_compatible_with(
tensor_shape.TensorShape(self._diag_update.shape)[-1])
_ops.broadcast_static_shape(
batch_shape, tensor_shape.TensorShape(self._diag_update.shape)[:-1])
