PAD = 34,
  UNIDIRECTIONAL_SEQUENCE_RNN = 35,
  GATHER = 36,
  BATCH_TO_SPACE_ND = 37,
  SPACE_TO_BATCH_ND = 38,
  TRANSPOSE = 39,
  MEAN = 40,
  SUB = 41,
  DIV = 42,
  SQUEEZE = 43,
  UNIDIRECTIONAL_SEQUENCE_LSTM = 44,
  STRIDED_SLICE = 45,
  BIDIRECTIONAL_SEQUENCE_RNN = 46,
  EXP = 47,
  TOPK_V2 = 48,
  SPLIT = 49,
  LOG_SOFTMAX = 50,

  %0 = "tf.Bitcast"(%arg0) : (tensor<2xf32>) -> tensor<2x2xf16>
  func.return %0 : tensor<2x2xf16>
}

// -----

// CHECK-LABEL: squeeze
func.func @squeeze(%arg0: tensor<1x1x10xf32>) -> tensor<1x10xf32> {
  // CHECK: mhlo.reshape
  %0 = "tf.Squeeze"(%arg0) : (tensor<1x1x10xf32>) -> tensor<1x10xf32>
  func.return %0 : tensor<1x10xf32>
}

// -----

// CHECK-LABEL: squeeze_ranked

  PAD = 34,
  UNIDIRECTIONAL_SEQUENCE_RNN = 35,
  GATHER = 36,
  BATCH_TO_SPACE_ND = 37,
  SPACE_TO_BATCH_ND = 38,
  TRANSPOSE = 39,
  MEAN = 40,
  SUB = 41,
  DIV = 42,
  SQUEEZE = 43,
  UNIDIRECTIONAL_SEQUENCE_LSTM = 44,
  STRIDED_SLICE = 45,
  BIDIRECTIONAL_SEQUENCE_RNN = 46,
  EXP = 47,
  TOPK_V2 = 48,
  SPLIT = 49,
  LOG_SOFTMAX = 50,

  const auto& expected_params = collected_quant_params[0];
  for (int i = 1; i < collected_quant_params.size(); ++i) {
    const auto& compared_params = collected_quant_params[i];
    // For some ops (such as Transpose or Squeeze), the quantized axis might not
    // be the same, this function only verifies the scale and zero point in
    // that case. The quantized axis should be verified in their own verifier
    // method.

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,

### Number of Processes on the Official Docker Image

The **number of processes** on this image is **computed automatically** from the CPU **cores** available.

This means that it will try to **squeeze** as much **performance** from the CPU as possible.

You can also adjust it with the configurations using **environment variables**, etc.

  %0 = "tfl.squeeze"(%arg0) {squeeze_dims = [0]}: (tensor<*xf32>) -> tensor<*xf32>
  func.return %0: tensor<*xf32>

// CHECK-LABEL: DontConvertSqueezeToReshape
// CHECK: %[[RESULT:.*]] = "tfl.squeeze"(%arg0)
// CHECK:  return %[[RESULT]]
}

func.func @ConvertSqueezeToReshapeOnMultiDynamicDims(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {

// CHECK-DAG:         %[[CST_1:.*]] = "tf.Const"() <{value = dense<[1, 1504]> : tensor<2xi32>}> : () -> tensor<2xi32>
// CHECK:             %[[VAL_2:.*]] = "tf.Squeeze"(%[[VAL_1]]) <{squeeze_dims = [0]}> : (tensor<1x2xi32>) -> tensor<2xi32>
// CHECK:             %[[VAL_3:.*]] = "tf.Slice"(%[[ARG_0]], %[[VAL_2]], %[[CST_1]]) : (tensor<1x2944xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<1x1504xi32>

  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 ["

	for len(out) > 0 {
		n := copy(out, d.storage[d.i:d.n])
		d.i += n
		out = out[n:]

		// Apply the permutation if we've squeezed the sponge dry.
		if d.i == d.rate {
			d.permute()
		}
	}

	return
}

// Sum applies padding to the hash state and then squeezes out the desired
// number of output bytes. It panics if any output has already been read.
func (d *state) Sum(in []byte) []byte {