//   [ 3.,  4.,  3.,  5.],
  //   [ 15.,  24.,  0.,  30.]
  // ]
  auto left = Cumprod(scope, reshaped, zero, Cumprod::Exclusive(true));

  // [
  //   [ 35.,  48.,  0.,  36.],
  //   [  7.,   8.,   5.,   6.],
  //   [  1.,   1.,   1.,   1.]
  // ]
  auto right =
      Cumprod(scope, reshaped, zero, Cumprod::Exclusive(true).Reverse(true));

  // left * right =
  // [
  //   [ 35.,  48.,  0.,  36.],

  func.return %0 : tensor<?xf32>
}

//===----------------------------------------------------------------------===//
// Cumprod op legalizations.
//===----------------------------------------------------------------------===//

// -----

// CHECK-LABEL: func @cumprod
func.func @cumprod(%arg0: tensor<4xf32>) -> tensor<4xf32> {
  // CHECK: [[INIT:%.*]] = mhlo.constant dense<1.000000e+00> : tensor<f32>

      "Conv",
      "Conv2D",
      "Conv2DBackpropFilter",
      "Conv2DBackpropInput",
      "Conv3D",
      "Conv3DBackpropFilterV2",
      "Conv3DBackpropInputV2",
      "Cross",
      "Cumprod",
      "Cumsum",
      "CumulativeLogsumexp",
      "DenseBincount",
      "DataFormatDimMap",
      "DataFormatVecPermute",
      "DepthToSpace",
      "DepthwiseConv2dNative",

  %0 = "tf.Cumprod"(%arg, %axis) : (tensor<8x16xf32>, tensor<2xi32>) -> tensor<8x16xf32>
  func.return %0 : tensor<8x16xf32>
}

// -----

func.func @testCumprod(%arg: tensor<8x16xf32>) -> tensor<8x16xf32> {
  %axis = arith.constant dense<-3> : tensor<i32>
  // expected-error @+1 {{axis operand should be within range [-2, 2)}}

By setting the `exclusive` kwarg to `True`, an exclusive cumprod is
performed instead:

```python
tf.cumprod([a, b, c], exclusive=True)  # => [1, a, a * b]
```

By setting the `reverse` kwarg to `True`, the cumprod is performed in the
opposite direction:

```python
tf.cumprod([a, b, c], reverse=True)  # => [a * b * c, b * c, c]
```

This is more efficient than using separate `tf.reverse` ops.

      result = rewriter.create<tensor::CastOp>(loc, op.getType(), result);
    rewriter.replaceOp(op, result);
    return success();
  }
};

// Converts the Cumsum or Cumprod TensorFlow op to the HLO ReduceWindow op by
// setting appropriate window dimensions, with the given aggregation op as the
// reduction function. The input tensor needs to have a static shape, and 'axis'

        Instead of converting probabilities to logits, we are using the cross
        entropy formula for probabilities.
    *   Added public APIs for `cumsum` and `cumprod` keras backend functions.
    *   Add support for temporal sample weight mode in subclassed models.
    *   Raise `ValueError` if an integer is passed to the training APIs.

// CHECK-DAG:       %[[VAL_2:.*]] = "tf.Const"() <{value = dense<1> : tensor<i64>}> : () -> tensor<i64>
// CHECK:           %[[VAL_3:.*]] = "tf.Cumprod"(%[[VAL_0]], %[[VAL_2]]) <{exclusive = false, reverse = false}> : (tensor<4x12xf32>, tensor<i64>) -> tensor<4x12xf32>
// CHECK:           return %[[VAL_3]] : tensor<4x12xf32>
// CHECK:         }