attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
        }
        int_val: 0
      }
    }
  }
}
node {
  name: "indexed_case"
  op: "StatelessCase"
  input: "Const_1"
  input: "Const"
  attr {
    key: "Tin"
    value {
      list {
        type: DT_INT32
      }
    }
  }
  attr {
    key: "Tout"
    value {
      list {

      tf_executor.fetch %outputs_0 : tensor<*xi32>
    }
    func.return %0 : tensor<*xi32>
  }
}

// CHECK: name: "stateless_case"
// CHECK-NEXT: "StatelessCase"
// CHECK: name: "regular_case"

    }
    ```
  }];

  let arguments = (ins
    I32Tensor:$branch_index,
    Variadic<TF_Tensor>:$input,

    ConfinedAttr<SymbolRefArrayAttr, [ArrayMinCount<1>]>:$branches,

    // Used to map StatelessCase and Case op defined in TensorFlow to a common
    // op.
    BoolAttr:$is_stateless
  );

  let results = (outs
    Variadic<TF_Tensor>:$output
  );

      "StatefulPartitionedCall",
      "StatefulStandardNormalV2",
      "StatefulTruncatedNormal",
      "StatefulUniform",
      "StatefulUniformFullInt",
      "StatefulUniformInt",
      "StatelessCase",
      "StatelessIf",
      "StatelessMultinomial",
      "StatelessParameterizedTruncatedNormal",
      "StatelessRandomGetAlg",
      "StatelessRandomGetKeyCounter",

    mlir::BoolAttr val = builder_.getBoolAttr(stateless);
    result.attributes.push_back(builder_.getNamedAttr("is_stateless", val));
  };

  // Map Case/If/While and StatelessCase/If/While op in TensorFlow to the common
  // Case/If/While op in MLIR and add the differentiating attribute.
  if (node.IsCaseNode()) composite_control_flow_op("Case");
  if (node.IsIfNode()) composite_control_flow_op("If");

*   Adds `tf.SparseTensor.with_values`. This returns a new SparseTensor with the
    same sparsity pattern, but with new provided values. It is similar to the
    `with_values` function of `RaggedTensor`.
*   Adds `StatelessCase` op, and uses it if none of case branches has stateful
    ops.
*   Adds `tf.config.experimental.get_memory_usage` to return total memory usage
    of the device.