mlir::cast<ShapedType>(input.getType()).getShape();
  ArrayRef<int64_t> output_shape =
      mlir::cast<ShapedType>(output.getType()).getShape();

  int64_t agg_value = 1;
  for (size_t i = agg_start_idx; i < input_shape.size() - 1; ++i) {
    agg_value *= input_shape[i];
  }

  return (agg_value == output_shape[agg_start_idx]);
}

// Returns whether the given type `a` is broadcast-compatible with `b`.

      for (auto s : new_shape) {
        shape.push_back(
            builder.getI32IntegerAttr(mlir::TFL::ConvertToTfliteSize(s)));
      }
      auto output_shape = DenseElementsAttr::get(shape_type, shape);
      auto shape_op = builder.create<tfl::ConstOp>(loc, output_shape);
      op_state.addOperands({shape_op});
    }

    op_state.addTypes({type});
  }

        // expected-remark@above {{ID: 0}}

def LegalizeSparseToDense : Pat<
  (TF_SparseToDenseOp $sparse_indices, $output_shape, $sparse_values,
    $default_value, $validate_indices),
  (TFL_SparseToDenseOp $sparse_indices, $output_shape, $sparse_values,
    $default_value)>;

def LegalizeUnique : Pat<(TF_UniqueOp $arg0),(TFL_UniqueOp $arg0)>;

  %4 = "tf.Case"(%2, %arg0, %arg1) {branches = [@sub, @add], output_shapes = [#tf_type.shape<>], device = "noodle", is_stateless = false} : (tensor<i32>, tensor<f32>, tensor<f32>) -> tensor<f32>
  // CHECK: PartitionedCall
  // CHECK-SAME: f = @sub
  // CHECK-SAME: _cluster_launch = "not_ready"
  // CHECK-SAME: device = "noodle"

      CompileTimeConstantOperand<TF::SegmentSumV2Op, 2>,   // $num_segments
      CompileTimeConstantOperand<TF::SliceOp, 1, 2>,       // $begin, $size
      CompileTimeConstantOperand<TF::SparseToDenseOp, 1>,  // $output_shape
      CompileTimeConstantOperand<TF::SplitOp, 0>,          // $split_dim
      // $size_splits, $split_dim
      CompileTimeConstantOperand<TF::SplitVOp, 1, 2>,

    %0:3 = "tf.While"(%cst_0, %cst_1, %arg0) {T = [i32, i32, f32], _lower_using_switch_merge = true, _num_original_outputs = 4 : i64, _read_only_resource_inputs = [], body = @while_body, cond = @while_cond, device = "", is_stateless = true, output_shapes = [#tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<1x5x5x1024>], parallel_iterations = 10 : i64, shape_invariant} : (tensor<i32>, tensor<i32>, tensor<1x5x5x1024xf32>) -> (tensor<i32>, tensor<i32>, tensor<1x5x5x1024xf32>)

  let summary = "A container for an iterator resource.";

  let arguments = (ins
    ConfinedAttr<TypeArrayAttr, [ArrayMinCount<1>]>:$output_types,
    ConfinedAttr<TF_ShapeAttrArray, [ArrayMinCount<1>]>:$output_shapes
  );

  let results = (outs
    Res<TF_ResourceTensor, [{A handle to the iterator that can be passed to a "MakeIterator" or
"IteratorGetNext" op. In contrast to Iterator, AnonymousIterator prevents

  Value none = rewriter.create<TFL::NoValueOp>(
      op->getLoc(), rewriter.getNoneType(), rewriter.getUnitAttr());

  Value output_shape =
      CreateCastToInt32(tf_op.getInputSizes(), op->getLoc(), rewriter);

  rewriter.replaceOpWithNewOp<TFL::Conv3DTransposeOp>(
      op, tf_op.getType(), output_shape, tf_op.getFilter(),
      tf_op.getOutBackprop(),
      /*bias=*/none, dilation_depth_factor, dilation_height_factor,

                .getScale(),
            /*filter_scales=*/filter_quantized_type.getScales());
        const ArrayRef<int64_t> output_shape =
            op->getResult(0).getType().cast<TensorType>().getShape();
        const SmallVector<int64_t, 1> bias_shape = {
            output_shape[output_shape.size() - 1]};
        // `tfl.fully_connected`'s `GetChannelDimIndex` is 0.
        const auto bias_quantized_type =