class_docstr: Represents the basis of a `TangentSpace`.
class_docstr_tok: ["Represents","the","basis","of","a","`","TangentSpace","`","."]
docstr: Returns densified version of this Basis.
  
  Returns:
    Tensor of shape `[N, B1, ..., Bn, D1, ... Dk], where
    `N` is the number of bases elements, `Bi` are possible
    batch dimensions, and `Di` are the dimensions of the
    tangent space (i.e. `R^{D1 x ... x Dk}`).
docstr_tok: ["Returns","densified","version","of","this","Basis",".","Returns",":","Tensor","of","shape","`","[","N",",","B1",",","...",",","Bn",",","D1",",","...","Dk","]",",","where","`","N","`","is","the","number","of","bases","elements",",","`","Bi","`","are","possible","batch","dimensions",",","and","`","Di","`","are","the","dimensions","of","the","tangent","space","(","i.e",".","`","R^","{","D1","x","...","x","Dk","}","`",")","."]
code: def to_dense(self):
      """Returns densified version of this Basis.
  
      Returns:
        Tensor of shape `[N, B1, ..., Bn, D1, ... Dk], where
        `N` is the number of bases elements, `Bi` are possible
        batch dimensions, and `Di` are the dimensions of the
        tangent space (i.e. `R^{D1 x ... x Dk}`).
      """
      return self._to_dense()
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 1 for calling the Basis obj's underlying member method code with example usage: obj.to_dense() and returns: self