// CHECK-DAG: %[[ONE:.*]] = mhlo.constant dense<1.000000e+00> : tensor<f32>
  // CHECK: %[[CONV:.*]] = mhlo.convert %arg0 : (tensor<3xi32>) -> tensor<3xi64>
  // CHECK: %[[F32:.*]] = "mhlo.rng"(%[[ZERO]], %[[ONE]], %[[CONV]]) {{.*UNIFORM.*}} -> tensor<12x?x64xf32>
  %0 = "tf.RandomUniform"(%arg0) : (tensor<3xi32>) -> tensor<12x?x64xf32>
  // CHECK: return %[[F32]]

            (i != out_batch_dim && out_type.isDynamicDim(i))) {
          return false;
        }
      }
    }

    // All ones in "lhs_dilation" means this "mhlo.conv" op should be
    // converted to "tf.Conv2D" or "tf.DepthwiseConv2dNativeOp".
    auto lhs_dilation = conv_op.getLhsDilation().value();
    if (!lhs_dilation.isSplat() || lhs_dilation.getSplatValue<int64_t>() != 1)

  // CHECK: %[[dq:.*]] = "tfl.dequantize"(%[[q]])
  // CHECK: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[dq]], %[[cst]])
  // CHECK: return %[[conv]] : tensor<256x8x7x3xf32>
}

// CHECK-LABEL: @fuseMulIntoFullyConnectedWithOptionalAttribute

			return false
		}
	}

	// DefaultLit is necessary for non-constants too: n might be 1.1<<k.
	n = DefaultLit(n, types.Types[types.TINT])
	*np = n

	return true
}

func Conv(n ir.Node, t *types.Type) ir.Node {
	if types.IdenticalStrict(n.Type(), t) {
		return n
	}
	n = ir.NewConvExpr(base.Pos, ir.OCONV, nil, n)
	n.SetType(t)
	n = Expr(n)
	return n
}

    value {
      type: DT_INT32
    }
  }
}
node {
  name: "training/SGD/gradients/loss_1/conv1/kernel/Regularizer/Square_grad/Mul_1"
  op: "Mul"
  input: "loss_1/conv1/kernel/Regularizer/Square/ReadVariableOp"
  input: "training/SGD/gradients/loss_1/conv1/kernel/Regularizer/Square_grad/Mul"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"
    value {

    // Replace filter uses with uniform quantized filter.
    rewriter.replaceAllUsesWith(filter_op->getResult(0),
                                quantized_filter_constant_op.getResult());

    // Replace conv op with a new convolution op that has quantized output type.
    // Quantize -> Dequantize following r3.
    auto output_uniform_quantize_call_op = cast<func::CallOp>(
        *combined_scale_multiply_op.getResult().user_begin());

    value {
      b: false
    }
  }
}
node {
  name: "training/LossScaleOptimizer/gradients/loss_1/conv1/kernel/Regularizer/Square_grad/Mul_1"
  op: "Mul"
  input: "loss_1/conv1/kernel/Regularizer/Square/ReadVariableOp"
  input: "training/LossScaleOptimizer/gradients/loss_1/conv1/kernel/Regularizer/Square_grad/Mul"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"

","statusText","finalText","crossDomain","host","hasContent","ifModified","headers","beforeSend","success","send","nativeStatusText","responses","isSuccess","response","modified","ct","finalDataType","firstDataType","ajaxHandleResponses","conv2","current","conv","dataFilter","throws","ajaxConvert","getJSON","getScript","text script","wrapAll","firstElementChild","wrapInner","htmlIsFunction","unwrap","visible","xhr","XMLHttpRequest","xhrSuccessStatus","0","1223","xhrSupported","cors","errorCallba...

","statusText","finalText","crossDomain","host","hasContent","ifModified","headers","beforeSend","success","send","nativeStatusText","responses","isSuccess","response","modified","ct","finalDataType","firstDataType","ajaxHandleResponses","conv2","current","conv","dataFilter","throws","ajaxConvert","getJSON","getScript","text script","wrapAll","firstElementChild","wrapInner","htmlIsFunction","unwrap","visible","xhr","XMLHttpRequest","xhrSuccessStatus","0","1223","xhrSupported","cors","errorCallba...

    // CHECK: %[[SHAPE:.*]] = "tf.Shape"
    // CHECK: %[[CONV:.*]] = "tf.Conv2DBackpropInput"(%[[SHAPE]]
    // CHECK-SAME: (tensor<4xi32>, tensor<1x1x1x1xf32>, tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32>
    // CHECK: return %[[CONV]] : tensor<1x1x1x1xf32>
    %0 = "tf.Shape"(%arg0) : (tensor<1x1x1x1xi32>) -> tensor<4xi32>
    %1 = "tf.Conv2DBackpropInput"(%0, %arg1, %arg1) {