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 ["

          {"MISC",
           // clang-format off
     {"ApproxTopK", "BroadcastTo", "ExpandDims", "Fill", "NoOp",
      "Range", "Rank", "Reshape", "Shape", "ShapeN", "Size", "Squeeze",
      "Transpose", "ZerosLike", "OnesLike", "BiasAdd" /*PW + Broadcast*/,
      "BroadcastArgs", "BroadcastGradientArgs", "OneHot", "Concat", "ConcatV2",

  func.return
}

// -----

func.func @testSqueezeOutOfBounds(%arg0: tensor<?x?x10xf32>) -> tensor<?x10xf32> {
  // expected-error @+1 {{squeeze dimension -4 not in [-3, 3)}}
  %0 = "tf.Squeeze"(%arg0) { squeeze_dims = [-4] }: (tensor<?x?x10xf32>) -> tensor<?x10xf32>
  func.return %0 : tensor<?x10xf32>
}

// -----

func.func @testTernaryEinsum(%arg0: tensor<2x3xf32>){

    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) {

// 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) {

```
# '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`.

beginning to grow larger again, and she thought at first she
would get up and leave the court; but on second thoughts she
decided to remain where she was as long as there was room for
her.

  `I wish you wouldn't squeeze so.' said the Dormouse, who was
sitting next to her.  `I can hardly breathe.'

  `I can't help it,' said Alice very meekly:  `I'm growing.'

  `You've no right to grow here,' said the Dormouse.

beginning to grow larger again, and she thought at first she
would get up and leave the court; but on second thoughts she
decided to remain where she was as long as there was room for
her.

  `I wish you wouldn't squeeze so.' said the Dormouse, who was
sitting next to her.  `I can hardly breathe.'

  `I can't help it,' said Alice very meekly:  `I'm growing.'

  `You've no right to grow here,' said the Dormouse.

      type: DT_FLOAT
    }
  }
  attr {
    key: "Tshape"
    value {
      type: DT_INT32
    }
  }
}
node {
  name: "metrics_2/sparse_categorical_accuracy/Squeeze"
  op: "Squeeze"
  input: "_identity_activation_49_target_1_0"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }