return nullptr;
}

// Follow the use chain of TensorList and return true iff all elements written
// to TensorList have same static shape. If all elements have same shape, assign
// it to `potential_element_type`.
//
// This can handle multiple mutations of a TensorList object and would return
// true if across all mutations the elements written have the same shape.
bool CanInferTensorListElementType(Value tensorlist,

    }
    return ImmutableEnumMap.asImmutable(enumMap);
  }

  /**
   * Returns a {@link Collector} that accumulates elements into an {@code ImmutableMap} whose keys
   * and values are the result of applying the provided mapping functions to the input elements. The
   * resulting implementation is specialized for enum key types. The returned map and its views will

    }
    return ImmutableEnumMap.asImmutable(enumMap);
  }

  /**
   * Returns a {@link Collector} that accumulates elements into an {@code ImmutableMap} whose keys
   * and values are the result of applying the provided mapping functions to the input elements. The
   * resulting implementation is specialized for enum key types. The returned map and its views will

`),
			Result: []byte(`
mergingList:
  - name: 4
    value: 4
  - name: 2
    value: 2
    other: b
  - name: 1
    other: a
`),
		},
	},
	{
		Description: "set elements order in a list",
		StrategicMergePatchRawTestCaseData: StrategicMergePatchRawTestCaseData{
			Original: []byte(`
mergingList:
  - name: 1
  - name: 3
    value: 3
  - name: 4
    value: 4

	if t1 != t2 {
		panic(what + ": " + t1.String() + " != " + t2.String())
	}
}

// arrayAt returns the i-th element of p,
// an array whose elements are eltSize bytes wide.
// The array pointed at by p must have at least i+1 elements:
// it is invalid (but impossible to check here) to pass i >= len,
// because then the result will point outside the array.

  |
  | o need to clean up all the descriptions, matching anything to the current project-descriptor.xml file and
  |   improving on that
  |
  | o use enums where appropriate (eg dependency scope)
  |
  | o a number of elements have a groupId/artifactId and sometimes version. It would be good to have them all extend one
  |   definition of these types
  |
-->

    return success();
  };
};

// Appends all elements in `range` to `values`.
template <typename ValueT, typename Range>
void Append(llvm::SmallVectorImpl<ValueT>& values, Range&& range) {
  values.insert(values.end(), range.begin(), range.end());
}

// Appends all elements in `range` to `values`.
template <typename ValueT, typename Range, typename... RangeTs>

get",props:{target:String,row:Boolean},data:{target:"> *",row:!0,forceHeight:!0},computed:{elements:function(t,e){return Ne(t.target,e)}},update:{read:function(){return{rows:(this.row?kn(this.elements):[this.elements]).map(En)}},write:function(t){t.rows.forEach(function(t){var i=t.heights;return t.elements.forEach(function(t,e){return Ve(t,"minHeight",i[e])})})},events:["resize"]}};function En(t){var e;if(t.length<2)return{heights:[""],elements:t};var i=An(t),n=i.heights,r=i.max,o=t.some(function(t){return...

      //   [arg0, arg5]:   these are states coming from ReduceDatasetOp's [arg1, arg6]
      //   [arg6: arg14]:  these are the input elements coming from the output of %51:9
      //   [arg15: arg35]: these are the captured elements from ReduceDatasetOp's [arg7:arg27]

}

// -----

func.func @testComplexAbsUnsupportedType(%arg0: tensor<?xf32>) -> tensor<?xf32> {
  // expected-error @+1 {{operand #0 must be tensor of complex type with 32-bit float elements or complex type with 64-bit float elements values}}
  %0 = "tfl.complex_abs"(%arg0): (tensor<?xf32>) -> tensor<?xf32>
  func.return %0 : tensor<?xf32>
}

// -----