}

Type GetTensorTypeForTensorList(Type element_type, TF::VariantType handle_dtype,
                                PatternRewriter *rewriter) {
  // If the variant type in the output handle has item shape available, use it
  // to derive the output shape by setting unknown leading dimension.
  // Otherwise, result type will be of unranked type.
  if (handle_dtype.getSubtypes().empty()) {
    return UnrankedTensorType::get(element_type);
  }

  let extraClassDeclaration = [{
    // Returns type of the TensorList element produced by this op.
    TensorType element_type() { return handle_dtype().getSubtypes()[0]; }

    // Returns data type of the result handle. Returned type contains type of
    // the TensorList element as a subtype.
    VariantType handle_dtype() {
      return getElementTypeOrSelf(getHandle().getType()).cast<TF::VariantType>();
    }
  }];
}

  if (a.handle_type == DT_INVALID) {
    result.handle_type = b.handle_type;
  } else if (b.handle_type == DT_INVALID) {
    result.handle_type = a.handle_type;
  } else if (a.handle_type == b.handle_type) {
    result.handle_type = a.handle_type;
  } else {
    return errors::InvalidArgument(
        "Mismatched resource types: ", DataTypeString(a.handle_type), " vs. ",

     tf_executor.fetch %outputs : tensor<8x1xf32>
  }
  func.return %0 : tensor<8x1xf32>
}

// Check that we generate _handle_dtypes and _handle_shapes for the resource
// argument.

// CHECK:      op: "_Arg"

// CHECK:        key: "_handle_dtypes"
// CHECK-NEXT:   value {
// CHECK-NEXT:     list {
// CHECK-NEXT:       type: DT_FLOAT
// CHECK-NEXT:     }
// CHECK-NEXT:   }
// CHECK-NEXT: }

     * @param handleType type of deployment handle
     * @param params constructor arguments
     *
     * @throws IllegalStateException if deployment handle with the given name already exists
     */
    <T extends DeploymentHandle> T start(String name, ChangeBehavior changeBehavior, Class<T> handleType, Object... params);

    /**

  // This is required to populate derived attributes during export in a
  // meaningful way. Else during export to GraphDef element_type() query
  // will result in out of bounds access/assert.
  if (handle_dtype().getSubtypes().size() != 1) {
    return emitOpError(
        "must have exactly one subtype in the result variant type");
  }

  if (!IsOfRankOrUnranked(op.getElementShape(), 0) &&

  // This is required to populate derived attributes during export in a
  // meaningful way. Else during export to GraphDef element_type() query
  // will result in out of bounds access/assert.
  if (handle_dtype().getSubtypes().size() != 1) {
    return emitOpError(
        "must have exactly one subtype in the result variant type");
  }
  if (!IsOfRankOrUnranked(op.getElementShape(), 0) &&

            lock.unlock();
        }
    }

    @Override
    public <T extends DeploymentHandle> T get(String name, Class<T> handleType) {
        lock.lock();
        try {
            failIfStopped();
            if (deployments.containsKey(name)) {
                return Cast.cast(handleType, deployments.get(name).getHandle());
            } else {
                return null;
            }
        } finally {

    if (auto tensor_array_v3_op = dyn_cast<TF::TensorArrayV3Op>(tf_op)) {
      Value handle = tensor_array_v3_op.getHandle();
      auto handle_type = mlir::cast<TensorType>(handle.getType());
      if (handle_type.getElementType().isInteger(/*width=*/32)) {
        Type resource_tensor_type =
            handle_type.clone(TF::ResourceType::get(rewriter.getContext()));
        handle.setType(resource_tensor_type);
      }
    }

  op: "_Arg"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "_handle_dtypes"
    value {
      list {
        type: DT_FLOAT
      }
    }
  }
  attr {
    key: "_handle_shapes"
    value {
      list {
        shape {
          dim {
            size: 1001