Unstack the values of a `Tensor` in the TensorArray. If input value shapes have rank-`R`, then the output TensorArray will contain elements whose shapes are rank-`(R-1)`. Args: value: (N+1)-D. Tensor of type `dtype`. The Tensor to unstack. name: A name for the operation (
(self, value, name=None)
| 1130 | |
| 1131 | @tf_should_use.should_use_result |
| 1132 | def unstack(self, value, name=None): |
| 1133 | """Unstack the values of a `Tensor` in the TensorArray. |
| 1134 | |
| 1135 | If input value shapes have rank-`R`, then the output TensorArray will |
| 1136 | contain elements whose shapes are rank-`(R-1)`. |
| 1137 | |
| 1138 | Args: |
| 1139 | value: (N+1)-D. Tensor of type `dtype`. The Tensor to unstack. |
| 1140 | name: A name for the operation (optional). |
| 1141 | |
| 1142 | Returns: |
| 1143 | A new TensorArray object with flow that ensures the unstack occurs. |
| 1144 | Use this object all for subsequent operations. |
| 1145 | |
| 1146 | Raises: |
| 1147 | ValueError: if the shape inference fails. |
| 1148 | """ |
| 1149 | return self._implementation.unstack(value, name=name) |
| 1150 | |
| 1151 | @tf_should_use.should_use_result |
| 1152 | def scatter(self, indices, value, name=None): |
no outgoing calls