// CHECK-NEXT: [[SQUEEZE:%.*]] = "tf.Squeeze"([[CONV]]) <{squeeze_dims = [3]}> : (tensor<1x128x128x1xf32>) -> tensor<1x128x128xf32>
  // CHECK-NEXT: [[RESULT:%.*]] = "tf.BiasAdd"([[SQUEEZE]], [[BIAS]]) : (tensor<1x128x128xf32>, tensor<128xf32>) -> tensor<1x128x128xf32>
  // CHECK-NEXT: return [[RESULT]] : tensor<1x128x128xf32>
}

// extra ops are used, so we detect the following patterns:
//
//
//   SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> BatchToSpaceND -> BiasAdd
//
//   SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> Pad -> BatchToSpaceND ->
//   BiasAdd
//
//   SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> BiasAdd -> BatchToSpaceND
//
//   SpaceToBatchND -> Conv2D -> Pad -> BatchToSpaceND -> BiasAdd
//

  int num_cols = tensorType.getShape()[rank - 1];

  std::vector<Value> sliced;

  if (batch_size == 1) {
    // Batch size is 1, no splitting is required
    // Squeeze the batch dimension, i.e. reshape
    // [1, num_rows, num_cols] -> [num_rows, num_cols]
    auto reshape_op = createReshapeOp(value, {num_rows, num_cols}, element_type,
                                      loc, rewriter);

  %0 = "tfl.squeeze"(%arg0) : (tensor<?x?xf32>) -> tensor<?x?xf32>
  // CHECK: return %arg0
  func.return %0 : tensor<?x?xf32>
}

// -----

// CHECK-LABEL: @squeeze_folder
func.func @squeeze_folder(%arg0 : tensor<?x?xf32>) -> tensor<*xf32> {
  %0 = "tfl.squeeze"(%arg0) : (tensor<?x?xf32>) -> tensor<*xf32>
  // CHECK: "tfl.squeeze"
  func.return %0 : tensor<*xf32>
}

// -----

  auto input_shape = Shape(scope, op.input(0));
  grad_outputs->push_back(Reshape(scope, grad_inputs[0], input_shape));
  return scope.status();
}
REGISTER_GRADIENT_OP("Squeeze", SqueezeGrad);

Status TransposeGrad(const Scope& scope, const Operation& op,
                     const std::vector<Output>& grad_inputs,
                     std::vector<Output>* grad_outputs) {

				continue
			}
			// Only clear key if there are pointers in it.
			if t.Key.Pointers() {
				if goarch.PtrSize == 8 {
					*(*unsafe.Pointer)(k) = nil
				} else {
					// There are three ways to squeeze at one or more 32 bit pointers into 64 bits.
					// Just call memclrHasPointers instead of trying to handle all cases here.
					memclrHasPointers(k, 8)
				}
			}

			hostTests: []nssHostTest{
				{"x.com", "myhostname", hostLookupFilesDNS},
				{"somehostname", "myhostname", hostLookupFilesDNS},
				{"myhostname", "myhostname", hostLookupCgo},
			},
		},
		// Debian Squeeze is just "dns,files", but lists all
		// the default criteria for dns, but then has a
		// non-standard but redundant notfound=return for the
		// files.
		{
			name:   "debian_squeeze",
			c:      &conf{},

        expanded_fft_len.getResult());

    // Insert the squeeze op.
    auto squeeze_dim = rewriter.getI64ArrayAttr({-2});
    TF::SqueezeOp squeeze = rewriter.create<TF::SqueezeOp>(
        rfft_op.getLoc(), output_type, rfft2d.getResult(), squeeze_dim);

    rewriter.replaceOp(op, squeeze.getResult());

    return success();
  }
};

  %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>

  %outputs_20 = "tf.ResizeBilinear"(%outputs_18, %outputs) {align_corners = false, device = "", half_pixel_centers = false} : (tensor<1x?x?x3xui8>, tensor<2xi32>) -> tensor<1x224x224x3xf32>
  %outputs_22 = "tf.Squeeze"(%outputs_20) {device = "", squeeze_dims = [0]} : (tensor<1x224x224x3xf32>) -> tensor<224x224x3xf32>
  %outputs_24 = "tf.Cast"(%outputs_22) {Truncate = false, device = ""} : (tensor<224x224x3xf32>) -> tensor<224x224x3xui8>