%0 = "tf.Squeeze"(%arg0) : (tensor<1x2x2xf32>) -> tensor<2x2xf32>
  func.return %0 : tensor<2x2xf32>

// CHECK-LABEL:squeezeDefault
// CHECK:  "tfl.squeeze"(%arg0) <{squeeze_dims = []}> : (tensor<1x2x2xf32>) -> tensor<2x2xf32>
}

func.func @squeezeSingleAxis(%arg0: tensor<2x1x2xf32>) -> tensor<2x2xf32> {
  %0 = "tf.Squeeze"(%arg0) {squeeze_dims = [1]} : (tensor<2x1x2xf32>) -> tensor<2x2xf32>

  auto float_graph = readonly_model_->subgraphs()->Get(0);
  // The original model reshape->custom->custom->squeeze.
  ASSERT_THAT(*float_graph->operators(), SizeIs(4));
  // The resulting model should be:
  // reshape->dequantize->custom->custom->quantize->squeeze.
  ASSERT_THAT(subgraph->operators, SizeIs(6));
  const std::vector<BuiltinOperator> op_codes = {

This operation is related to `squeeze()`, which removes dimensions of
size 1.
  }];

  // TODO: Restriction on dim's size and valid range are not modeled here.
  let arguments = (ins AnyTensor:$input, TFL_I32OrI64Tensor:$dim);

  let results = (outs AnyTensor:$output);

  let hasOptions = 1;
}

def TFL_SqueezeOp: TFL_Op<"squeeze", [Pure,

    SmallVector<int64_t, 4> begin_indices(value_rank, 0);
    auto end_indices = llvm::to_vector<4>(value_type.getShape());
    SmallVector<int64_t, 4> strides(value_rank, 1);

    // All HLO slice+squeeze results used to replace the original tf.Unpack op.
    SmallVector<Value, 4> results;
    results.reserve(op.getNumResults());

    for (int i = 0, end = op.getNumResults(); i < end; ++i) {

```
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t)) ==> [2, 3]
```

Or, to remove specific size 1 dimensions:

```
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
```
  }];

  let arguments = (ins
    Arg<TF_Tensor, [{The `input` to squeeze.}]>:$input,

    *   `parallel_for`: Add converters for `OneHot`, `LowerBound`, `UpperBound`.
    *   `parallel_for`: add converter for `BroadcastTo`.
    *   Add `pfor` converter for `Squeeze`.
    *   Add `RaggedTensor.placeholder()`.
    *   Add ragged tensor support to `tf.squeeze`.
    *   Update RaggedTensors to support int32 row_splits.
    *   Allow `LinearOperator.solve` to take a `LinearOperator`.