// input is a MLIR built-in tensor type.
    Attribute getInputElementType() {
      if (auto ty = getArg().getType().dyn_cast<TensorType>()) {
        return TypeAttr::get(ty.getElementType());
      }
      return {};
    }
  }];

  let hasCanonicalizer = 1;
}

def TFR_GetShapeOp : TFR_Op<"get_shape", [Pure]> {
  let description = [{

  ASSERT_THAT(func_op, NotNull());

  auto uniform_quantize_op =
      *func_op.getOps<::mlir::stablehlo::UniformQuantizeOp>().begin();
  Value result = uniform_quantize_op.getResult();
  EXPECT_THAT(GetElementType(result), NotNull());
}

}  // namespace
}  // namespace quant

  if (!sub_type) return false;

  bool has_string = llvm::any_of(sub_type.GetSubtypes(), [](TensorType type) {
    return mlir::isa<mlir::TF::StringType>(type.getElementType());
  });
  return has_string;
}

bool HasStringOperand(Operation& op) {
  for (auto operand : op.getOperands()) {
    auto operand_type = getElementTypeOrSelf(operand);

      // For type attributes, we only propagate the element type.
      mlir::Type elt_type = attr.getValue();
      if (auto shaped_type = mlir::dyn_cast<mlir::ShapedType>(elt_type)) {
        elt_type = shaped_type.getElementType();
      }
      TF_RETURN_IF_ERROR(
          ConvertAttribute(elt_type, remove_ref_type, &attr_val));
      list->add_type(attr_val.type());
    } else if (auto attr = mlir::dyn_cast<mlir::TF::ShapeAttr>(a)) {

  }
  OpBuilder builder(stack);
  Value buffer;
  if (failed(cutil::CreateInitBufferValue(
          elem_type->getShape(), stack.getMaxSize(), stack,
          elem_type->getElementType(), builder, &buffer))) {
    return failure();
  }
  auto size_var_type = GetSizeVarType(builder);
  auto var_type = RankedTensorType::get(
      {}, TF::ResourceType::get(

  DerivedTFLiteTypeAttr output_type = DerivedTFLiteTypeAttr<[{
    return getResult().getType().cast<TensorType>().getElementType().
        cast<IntegerType>().getWidth() > 32 ? tflite::TensorType_INT64 :
            tflite::TensorType_INT32;
    }], [{
      TypeAttr::get(getResult().getType().cast<TensorType>().getElementType())
    }]>;
}

def TFL_ArgMinOp : TFL_Op<"arg_min", [
    QuantizableResult,
    Pure]> {

  for (auto it : llvm::enumerate(var_access_info.new_operand_values)) {
    Type type = it.value().getType();
    if (type.isa<TensorType>() &&
        type.cast<TensorType>().getElementType().isa<TF::ResourceType>()) {
      if (!llvm::is_contained(device_var_reads_indices, it.index()) &&
          !llvm::is_contained(device_var_updates_indices, it.index())) {

  return types;
}

bool IsResourceType(Type val_type) {
  if (auto tensor_type = mlir::dyn_cast<mlir::TensorType>(val_type)) {
    if (mlir::isa<TF::ResourceType>(tensor_type.getElementType())) {
      return true;
    }
  }
  return false;
}

bool IsTPUOp(mlir::Operation* op) {
  return op->hasAttr(TF::kReplicationInfoAttr);
}

      if (dims[dim] == mlir::ShapedType::kDynamic) {
        dims[dim] = encoding.getBounds()[dim];
      }
    }
  }
  return GetTypeFromTFTensorShape(dims, ranked_ty.getElementType());
}

// Calculates computation output shape and build OutputDescription for each
// output based on static shapes in MLIR module. If an output is a resource

        given:
        addToContainer(b)
        container.all {
            seen << it
        }

        when:
        container.addAllLater(provider)

        then:
        _ * provider.getElementType() >> getType()
        _ * provider.size() >> 2
        _ * provider.calculateValue(_) >> ValueSupplier.Value.of([a, d])
        0 * _
        seen == [b, a, d]
    }