%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>

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,

  if (!input_type) return success();  // Can't verify squeeze dims.

  int64_t input_rank = input_type.getRank();
  for (const auto &squeeze_dim_apint :
       op.getSqueezeDims().getAsValueRange<IntegerAttr>()) {
    int64_t squeeze_dim = squeeze_dim_apint.getSExtValue();
    if (squeeze_dim < -input_rank || squeeze_dim >= input_rank) {
      return op.emitOpError()
             << "squeeze dimension " << squeeze_dim << " not in ["

// TFL_TransposeOp when the tensor has some dimensions with value==1
// Example- "tfl.transpose"(tensor<56x8x56x1x1x1x7xf32>, [4, 5, 1, 2, 0, 6, 3])
// Permutation before squeese is [4, 5, 1, 2, 0, 6, 3] becomes [1, 2, 0, 3]
// after squeeze is perfomed to retain the relative ordering of the non-1 dims.
DenseElementsAttr GetSqueezedPermutation(Value input_value,
                                         Value input_permutation) {