}

// -----

func.func @testConv2D(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x2x16xf32>) -> tensor<256x32x32x16xf32> {
  // expected-error @+1 {{requires the number of input channels to be divisible by the number of filter input channels; found 3 and 2, respectively}}
  %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x2x16xf32>) -> tensor<256x32x32x16xf32>

    // 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 =

  if (op.getNumResults() == 0) return failure();

  if (input_dim_size % op.getNumResults() != 0)
    return op.emitOpError("dimension #")
           << *dim_index << " not divisible by the number of result tensors";

  return success();
}

//===----------------------------------------------------------------------===//
// SplitVOp

    // expected-error@+1 {{incorrect input sharding configuration received. 1-th dimension of the input must be evenly divisible by 4}}

    %strides = "tf.Const"() {value = dense<[3, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
    // expected-error @below {{'tf.XlaConvV2' op Expected output feature dimension (value 9) to be divisible by feature_group_count (value 2).}}

  }];

  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'.
		}

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'.
		}

perceive the Species of Things in its Sensorium, but only for conveying
them thither; and God has no need of such Organs, he being every where
present to the Things themselves. And since Space is divisible _in
infinitum_, and Matter is not necessarily in all places, it may be also
allow'd that God is able to create Particles of Matter of several Sizes
and Figures, and in several Proportions to Space, and perhaps of

Organs of Sense are not for enabling the Soul to perceive the Species of Things in its Sensorium, but only for conveying them thither; and God has no need of such Organs, he being every where present to the Things themselves. And since Space is divisible _in infinitum_, and Matter is not necessarily in all places, it may be also allow'd that God is able to create Particles of Matter of several Sizes and Figures, and in several Proportions to Space, and perhaps of different Densities and Forces, and...