}
	}
	if pagesPerArena%pagesPerSpanRoot != 0 {
		print("pagesPerArena (", pagesPerArena, ") is not divisible by pagesPerSpanRoot (", pagesPerSpanRoot, ")\n")
		throw("bad pagesPerSpanRoot")
	}
	if pagesPerArena%pagesPerReclaimerChunk != 0 {
		print("pagesPerArena (", pagesPerArena, ") is not divisible by pagesPerReclaimerChunk (", pagesPerReclaimerChunk, ")\n")
		throw("bad pagesPerReclaimerChunk")
	}

                            << kernel_output_features << ") to be divisible by "
                            << "feature_group_count (value "
                            << feature_group_count_val << ").\n";
  }
  if (input_batch % batch_group_count != 0) {
    return op.emitOpError()
           << "Expected input batch dimension (value " << input_batch
           << " ) to be divisible by batch_group_count (value "

    // all spatial dimensions.
    const int64_t filter_channels = GetDimSize(filter_ty, num_spatial_dims);
    // TensorFlow convolution op verifies that the number of input channels is
    // divisible by the number of filter channels.
    // For depthwise convolution the feature_group_count argument would be set
    // to the input feature dimension.
    const int64_t feature_group_count =

  }];

  let arguments = (ins
    Arg<TF_Tensor, [{4-D tensor with shape
`[batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
  depth]`. Note that the batch size of the input tensor must be divisible by
`block_size * block_size`.}]>:$input,
    Arg<TF_I32OrI64Tensor, [{2-D tensor of non-negative integers with shape `[2, 2]`. It specifies
how many elements to crop from the intermediate result across the spatial

func generate(ch chan<- int) {
	for i := 2; ; i++ {
		ch <- i  // Send 'i' to channel 'ch'.
	}
}

// Copy the values from channel 'src' to channel 'dst',
// removing those divisible by 'prime'.
func filter(src <-chan int, dst chan<- int, prime int) {
	for i := range src {  // Loop over values received from 'src'.
		if i%prime != 0 {
			dst <- i  // Send 'i' to channel 'dst'.
		}