return results;
}

// A struct to hold axes and sizes for a set of dimensions.
struct DimensionVector {
  llvm::ArrayRef<int64_t> AxesArray() const { return axes; }
  llvm::ArrayRef<int64_t> SizesArray() const { return sizes; }

  llvm::SmallVector<int64_t, 4> axes;
  llvm::SmallVector<int64_t, 4> sizes;
};

// Create a tensor that is reshaped from input.

  %sizes = "tf.Const"() {value = dense<[1, 4]> : tensor<2xi64>} : () -> (tensor<2xi64>)
  %0 = "tf.Slice"(%arg0, %arg1, %sizes) : (tensor<3x4xi32>, tensor<2xi64>, tensor<2xi64>) -> tensor<1x4xi32>
  func.return %0 : tensor<1x4xi32>
}

// -----

func.func @testSlice_begin_2d(%arg0: tensor<4xi32>, %begins: tensor<2x2xi64>) -> tensor<3xi32> {

left: rect.left,\n    top: rect.top,\n    width: rect.right - rect.left,\n    height: rect.bottom - rect.top,\n  };\n\n  // subtract scrollbar size from sizes\n  const sizes = element.nodeName === 'HTML' ? getWindowSizes(element.ownerDocument) : {};\n  const width =\n    sizes.width || element.clientWidth || result.width;\n  const height =\n    sizes.height || element.clientHeight || result.height;\n\n  let horizScrollbar = element.offsetWidth - width;\n  let vertScrollbar = element.offsetHeight - height;\n\n...

:Number,height:Number,offsetTop:String,offsetLeft:String,target:String},data:{dataSrc:"",dataSrcset:!1,sizes:!1,width:!1,height:!1,offsetTop:"50vh",offsetLeft:0,target:!1},computed:{cacheKey:function(t){var e=t.dataSrc;return this.$name+"."+e},width:function(t){var e=t.width,i=t.dataWidth;return e||i},height:function(t){var e=t.height,i=t.dataHeight;return e||i},sizes:function(t){var e=t.sizes,i=t.dataSizes;return e||i},isImg:function(t,e){return Zn(e)},target:{get:function(t){var e=t.target;ret...

    return op.emitOpError() << "requires N to be at least 2, got " << op.getN();

  if (op.getShape().size() != op.getOffset().size())
    return op.emitOpError()
           << "requires sizes of shapes and offsets to be the same, got sizes "
           << op.getShape().size() << " and " << op.getOffset().size();

  auto ranked_dim =
      mlir::dyn_cast<RankedTensorType>(op.getConcatDim().getType());

        return op.emitOpError(
            "cannot have more than one dynamic dimension in split sizes");
      dynamic_dim_index = dim.index();
    } else {
      total_dim_size += dim_val;
    }
  }

  if (!dynamic_dim_index && total_dim_size != input_dim_size)
    return op.emitOpError(
               "split sizes must sum up to the dimension size along split "
               "dimension, found ")

// Verify operand types and the result type:
//
// 1. Operand type ranks must be equal to the output type rank.
//
// 2. Operand dimension sizes (except dimension `axis`) must be equal to
//    previously seen dimension sizes of the same dimension.
//
// 3. Sum of operand dimension sizes of the `axis` dimension must be equal to
//    the dimension size of the `axis` dimension of output.
//

		Help:      "Distribution of object sizes in the bucket, includes label for the bucket name",
		Type:      histogramMetric,
	}
}

func getBucketObjectVersionsMD() MetricDescription {
	return MetricDescription{
		Namespace: bucketMetricNamespace,
		Subsystem: objectsSubsystem,
		Name:      versionDistribution,
		Help:      "Distribution of object sizes in the bucket, includes label for the bucket name",

	MOVOU (15*16)(inp), D0; PXOR tmpStore, D0; MOVOU D0, (15*16)(oup)
	LEAQ  256(inp), inp
	LEAQ  256(oup), oup
	SUBQ  $256, inl
	JMP   openSSEMainLoop

openSSEMainLoopDone:
	// Handle the various tail sizes efficiently
	TESTQ inl, inl
	JE    openSSEFinalize
	CMPQ  inl, $64
	JBE   openSSETail64
	CMPQ  inl, $128
	JBE   openSSETail128
	CMPQ  inl, $192
	JBE   openSSETail192
	JMP   openSSETail256

  let summary = "Select operator";

  let description = [{
    Select values of 'x' if the corresponding value of 'condition' is true or
    the value of 'y' if false. There are valid condition input sizes:

    1. Either the same shape (in which case the select is elementwise), or
    2. condition must be Rank 1 and match over the first dimension.
  }];

  let arguments = (ins