tf.self_adjoint_eig(tensor, name=None) Computes the eigen decomposition of a batch of self-adjoint matrices.

tf.reduce_any(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the "logical or" of elements across dimensions

tf.maximum(x, y, name=None) Returns the max of x and y (i.e. x > y ? x : y) element-wise. NOTE:

tf.reduce_all(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the "logical and" of elements across

tf.reduce_min(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the minimum of elements across dimensions

tf.reduce_logsumexp(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes log(sum(exp(elements across dimensions

tf.lbeta(x, name='lbeta') Computes ln(|Beta(x)|), reducing along the last dimension. Given

tf.add(x, y, name=None) Returns x + y element-wise. NOTE: Add supports broadcasting

tf.cross(a, b, name=None) Compute the pairwise cross product. a and b

tf.ifft2d(input, name=None) Compute the inverse 2-dimensional discrete Fourier Transform over the inner-most 2