parameters: (tensor, name=None)
return_statement: return e, v
docstring: Computes the eigen decomposition of a batch of self-adjoint matrices.
  
    Computes the eigenvalues and eigenvectors of the innermost N-by-N matrices
    in `tensor` such that
    `tensor[...,:,:] * v[..., :,i] = e[..., i] * v[...,:,i]`, for i=0...N-1.
  
    Args:
      tensor: `Tensor` of shape `[..., N, N]`. Only the lower triangular part of
        each inner inner matrix is referenced.
      name: string, optional name of the operation.
  
    Returns:
      e: Eigenvalues. Shape is `[..., N]`. Sorted in non-decreasing order.
      v: Eigenvectors. Shape is `[..., N, N]`. The columns of the inner most
        matrices contain eigenvectors of the corresponding matrices in `tensor`
docstring_summary: Computes the eigen decomposition of a batch of self-adjoint matrices.
docstring_tokens: Computes the eigen decomposition of a batch of self - adjoint matrices .
function: def self_adjoint_eig(tensor, name=None):
    """Computes the eigen decomposition of a batch of self-adjoint matrices.
  
    Computes the eigenvalues and eigenvectors of the innermost N-by-N matrices
    in `tensor` such that
    `tensor[...,:,:] * v[..., :,i] = e[..., i] * v[...,:,i]`, for i=0...N-1.
  
    Args:
      tensor: `Tensor` of shape `[..., N, N]`. Only the lower triangular part of
        each inner inner matrix is referenced.
      name: string, optional name of the operation.
  
    Returns:
      e: Eigenvalues. Shape is `[..., N]`. Sorted in non-decreasing order.
      v: Eigenvectors. Shape is `[..., N, N]`. The columns of the inner most
        matrices contain eigenvectors of the corresponding matrices in `tensor`
    """
    e, v = gen_linalg_ops.self_adjoint_eig_v2(tensor, compute_v=True, name=name)
    return e, v
function_tokens: def self_adjoint_eig ( tensor , name = None ) : e , v = gen_linalg_ops . self_adjoint_eig_v2 ( tensor , compute_v = True , name = name ) return e , v