```
With `src_format` of `NHWC`, `dst_format` of `NCHW`, and input:
```
[1, 2]
```
, the output will be:
```
[1, 2]
```
  }];

  let arguments = (ins
    Arg<TF_I32OrI64Tensor, [{Tensor of rank 1 or 2 in source data format.}]>:$x,

    DefaultValuedOptionalAttr<StrAttr, "\"NHWC\"">:$src_format,
    DefaultValuedOptionalAttr<StrAttr, "\"NCHW\"">:$dst_format
  );

  bool is_pool2d = rank == 4;
  if (verify_batch_channel_dims(0, rank - 1)) {
    *data_format = is_pool2d ? "NHWC" : "NDHWC";
  } else if (verify_batch_channel_dims(0, 1)) {
    *data_format = is_pool2d ? "NCHW" : "NCDHW";
  } else {
    return false;
  }

  if (rw.getWindowDilations().has_value() &&
      !(rw.getWindowDilations()->isSplat() &&
        rw.getWindowDilations()->getSplatValue<APInt>() == 1))

// CHECK-SAME:                                %[[VAL_0:.*]]: tensor<4x3x16x16xf32>) -> tensor<4x3x7x7xf32> {
// CHECK:           %[[VAL_1:.*]] = "tf.AvgPool"(%[[VAL_0]]) <{data_format = "NCHW", ksize = [1, 1, 3, 3], padding = "VALID", strides = [1, 1, 2, 2]}> : (tensor<4x3x16x16xf32>) -> tensor<4x3x7x7xf32>
// CHECK:           return %[[VAL_1]] : tensor<4x3x7x7xf32>
// CHECK:         }

// is handled, which can optionally require a general broadcast of the
// 'bias' term in a way that is not compatible with the standard left-padded
// broadcast semantics (i.e. NCHW will broadcast into dimension 1).
// The correct 'bias' broadcast will be synthesized manually.
class ConvertBiasAddOp : public OpRewritePattern<TF::BiasAddOp> {
 public:
  using OpRewritePattern::OpRewritePattern;

*   Add Mel-Frequency Cepstral Coefficient support to `tf.contrib.signal` (with
    GPU and gradient support).
*   Add a self-check on `import tensorflow` for Windows DLL issues.
*   Add NCHW support to `tf.depth_to_space` on GPU.
*   TensorFlow Debugger (tfdbg):
    *   Add `eval` command to allow evaluation of arbitrary Python/numpy
        expressions in tfdbg command-line interface. See