r:#ececec}.bg-gradient-danger.btn:hover{background:#c82333 linear-gradient(180deg,#d04451,#c82333) repeat-x!important}.bg-gradient-danger.btn.active,.bg-gradient-danger.btn:active,.bg-gradient-danger.btn:not(:disabled):not(.disabled).active,.bg-gradient-danger.btn:not(:disabled):not(.disabled):active{border-color:#b21f2d;color:#fff}.bg-gradient-danger.btn.active,.bg-gradient-danger.btn:active,.bg-gradient-danger.btn:not(:disabled):not(.disabled).active,.bg-gradient-danger.btn:not(:disabled):not(...

*   Unified eager and `tf.function` execution:

    *   Eager mode can now execute each op as a `tf.function`, allowing for more
        consistent feature support in future releases.
    *   It is available for immediate use.
        *   See the `TF_RUN_EAGER_OP_AS_FUNCTION` environment variable in

.table-warning:hover {\n  background-color: #ffe8a1;\n}\n\n.table-hover .table-warning:hover > td,\n.table-hover .table-warning:hover > th {\n  background-color: #ffe8a1;\n}\n\n.table-danger,\n.table-danger > th,\n.table-danger > td {\n  background-color: #f5c6cb;\n}\n\n.table-danger th,\n.table-danger td,\n.table-danger thead th,\n.table-danger tbody + tbody {\n  border-color: #ed969e;\n}\n\n.table-hover .table-danger:hover {\n  background-color: #f1b0b7;\n}\n\n.table-hover .table-danger:hover > td,\n.table-hover...

.table-warning:hover {\n  background-color: #ffe8a1;\n}\n\n.table-hover .table-warning:hover > td,\n.table-hover .table-warning:hover > th {\n  background-color: #ffe8a1;\n}\n\n.table-danger,\n.table-danger > th,\n.table-danger > td {\n  background-color: #f5c6cb;\n}\n\n.table-danger th,\n.table-danger td,\n.table-danger thead th,\n.table-danger tbody + tbody {\n  border-color: #ed969e;\n}\n\n.table-hover .table-danger:hover {\n  background-color: #f1b0b7;\n}\n\n.table-hover .table-danger:hover > td,\n.table-hover...

in situations where the iterator does not block the calling thread, or where
the calling thread is not a member of the thread pool used to execute parallel
operations (e.g. in eager mode).
  }];

  let arguments = (ins
    Arg<TF_ResourceTensor, "", [TF_DatasetIteratorRead, TF_DatasetIteratorWrite]>:$iterator
  );

  let results = (outs
    Variadic<TF_Tensor>:$components