int cpus = Runtime.getRuntime().availableProcessors();
        assertEquals((int) (cpus * 2.2), cli.calculateDegreeOfConcurrency("2.2C"));
        assertEquals(1, cli.calculateDegreeOfConcurrency("0.0001C"));
        assertThrows(IllegalArgumentException.class, () -> cli.calculateDegreeOfConcurrency("-2.2C"));
        assertThrows(IllegalArgumentException.class, () -> cli.calculateDegreeOfConcurrency("0C"));
    }

# ADR-0001 - Use Architectural Decision Records

## Date

2023-12-01

## Context

In a distributed team with many subteams, the best solution to communicate decisions is to use a format accessible by everyone in charge of development.

We use *Specification* and *Discovery* documents stored in Google Drive, but they present some downsides:

        assertEquals(0, new BigDecimal("0.001").compareTo(BigDecimalConversionUtil.toBigDecimal(new Double(0.001D))));
        assertEquals(0, new BigDecimal("0.0001").compareTo(BigDecimalConversionUtil.toBigDecimal(new Double(0.0001D))));
        assertEquals(0, new BigDecimal("0.00001").compareTo(BigDecimalConversionUtil.toBigDecimal(new Double(0.00001D))));

  %0:2 = "tfl.split"(%cst, %arg0) {num_splits = 2 : i32} : (tensor<i32>, tensor<4xf32>) -> (tensor<2xf32>, tensor<2xf32>)
    loc(fused["skip1", "skip2.cc":10:8, callsite("op" at "skip3.cc":10:8)])
  func.return %0#0, %0#1 : tensor<2xf32>, tensor<2xf32>

// CHECK-NEXT: "tfl.split"
// CHECK-NEXT: return
}

// CHECK-LABEL: import_stats_name

// CHECK-NEXT: %[[Y:[a-z0-9]*]], {{.*}}_FusedBatchNormEx
// CHECK-NEXT: return %[[Y]]
  %y:6 = "tf.FusedBatchNormV3"(%arg0, %arg1, %arg2, %arg3, %arg4) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", epsilon = 0.001 : f32, is_training = false} : (tensor<8x8x8x8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>) -> (tensor<8x8x8x8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>)

//   %0:6 = "tf.FusedBatchNormV3"(%cst_0, %cst_1, %cst_2, %cst_1, %cst_2) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", epsilon = 0.001 : f32, is_training = false} : (tensor<1x1x2x8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>) -> (tensor<1x1x2x8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>)

    %ro:2, %c2 = tf_executor.island wraps "tf.TPUReplicatedOutput"(%out) : (tensor<i32>) -> (tensor<i32>, tensor<i32>)
    tf_executor.fetch %ro#0, %ro#1 : tensor<i32>, tensor<i32>
  }
  return %0#0, %0#1 : tensor<i32>, tensor<i32>
}

// -----

func.func @num_replicas_replicated_input(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>) -> (tensor<i32>, tensor<i32>) {

    "tf.Yield"(%3, %4) : (tensor<i32>, tensor<i32>) -> ()
  }) {is_stateless = true, parallel_iterations = 10 : i64} : (tensor<i32>, tensor<i32>) -> (tensor<i32>, tensor<i32>)
  return %0#0, %0#1 : tensor<i32>, tensor<i32>
}

// -----

// Check that `tf.ReadVariableOp` is hoisted because the variable is readonly.
// The following ops that depend on `tf.ReadVariableOp` and satisfy hoisting

(0x0,0x0)
(0x0,0x0)
0x0
[0/0]0x0
-7
7
7
7
7
7
7
+8.000000e+000
(+9.000000e+000+1.000000e+001i)
true
false
hello
one two
one two
hello
false
true
(+1.400000e+001+1.500000e+001i)
+1.300000e+001
12
12
12
12
12
12
-11
[0/0]0x0
0x0
(0x0,0x0)

	  tf_device.return %3#0, %3#1 : tensor<2048xi32>, tensor<2048xi32>
	}) {device = "TPU_REPLICATED_HOST_0"} : () -> (tensor<2048xi32>, tensor<2048xi32>)
    return %0#0, %0#1: tensor<2048xi32>, tensor<2048xi32>
  }

  // CHECK-LABEL: func @valid_copy_op_in_non_replicated_host

  // CHECK: "tf_device.launch"
  // CHECK-SAME: "/job:localhost/replica:0/task:0/device:CPU:0"