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Weighted interpolation for M-D point sets.

Given an M-D point set, this function can be used to generate a new point set that is formed by interpolating a subset of points in the set.


In the following, A1 to An, and B1 to Bk are optional batch dimensions.

points A tensor with shape [B1, ..., Bk, M] and rank R > 1, where M is the dimensionality of the points. </td> </tr><tr> <td>weights</td> <td> A tensor with shape[A1, ..., An, P], where P is the number of points to interpolate for each output point. </td> </tr><tr> <td>indices</td> <td> A tensor of dtype tf.int32 and shape[A1, ..., An, P, R-1], which contains the point indices to be used for each output point. The R-1 dimensional axis gives the slice index of a single point inpoints. The first n+1 dimensions of weights and indices must match, or be broadcast compatible. </td> </tr><tr> <td>normalize</td> <td> Abooldescribing whether or not to normalize the weights on the last axis. </td> </tr><tr> <td>allow_negative_weights</td> <td> Abooldescribing whether or not negative weights are allowed. </td> </tr><tr> <td>name` A name for this op. Defaults to "weighted_interpolate".

A tensor of shape [A1, ..., An, M] storing the interpolated M-D points. The first n dimensions will be the same as weights and indices.