Wrap all random_init calls with directives so they don't execute

when compiling with nagfor. These directives should be removed
when, as expected, nagfor provides support for `random_init` in
an upcoming version.

example/learn-addition.f90

@@ -47,7 +47,9 @@ program learn_addition
     type(bin_t), allocatable :: bins(:)
     real, allocatable :: cost(:), random_numbers(:)
 
+#ifndef NAGFOR
     call random_init(image_distinct=.true., repeatable=.true.)
+#endif
 
     trainable_engine = perturbed_identity_network(perturbation_magnitude=0.05)
     call output(trainable_engine%to_inference_engine(), string_t("initial-network.json"))

example/learn-exponentiation.f90

@@ -47,7 +47,9 @@ program learn_exponentiation
     type(bin_t), allocatable :: bins(:)
     real, allocatable :: cost(:), random_numbers(:)
 
+#ifndef NAGFOR
     call random_init(image_distinct=.true., repeatable=.true.)
+#endif
 
     trainable_engine = perturbed_identity_network(perturbation_magnitude=0.05)
     call output(trainable_engine%to_inference_engine(), string_t("initial-network.json"))
@@ -60,10 +62,10 @@ program learn_exponentiation
         inputs = [(tensor_t(real([(j*i, j = 1,num_inputs)])/(num_inputs*num_pairs)), i = 1, num_pairs)]
         desired_outputs = y(inputs)
         output_sizes = [(size(desired_outputs(i)%values()),i=1,size(desired_outputs))]
-        call assert(all([num_outputs==output_sizes]), "fit-polynomials: # outputs", intrinsic_array_t([num_outputs,output_sizes])) 
+        call assert(all([num_outputs==output_sizes]), "fit-polynomials: # outputs", intrinsic_array_t([num_outputs,output_sizes]))
       end block
       input_output_pairs = input_output_pair_t(inputs, desired_outputs)
-      block 
+      block
         integer b
         bins = [(bin_t(num_items=num_pairs, num_bins=num_mini_batches, bin_number=b), b = 1, num_mini_batches)]
       end block

example/learn-microphysics-procedures.f90

@@ -39,7 +39,9 @@ program learn_microphysics_procedures
     integer, parameter :: nodes_per_layer(*) = [2, 72, 2]
     real, parameter :: cost_tolerance = 1.E-08
 
+#ifndef NAGFOR
     call random_init(image_distinct=.true., repeatable=.true.)
+#endif
 
     open(newunit=network_unit, file=network_file%string(), form='formatted', status='old', iostat=io_status, action='read')
 

example/learn-multiplication.f90

@@ -47,7 +47,9 @@ program learn_multiplication
     type(bin_t), allocatable :: bins(:)
     real, allocatable :: cost(:), random_numbers(:)
 
+#ifndef NAGFOR
     call random_init(image_distinct=.true., repeatable=.true.)
+#endif
 
     trainable_engine = perturbed_identity_network(perturbation_magnitude=0.05)
     call output(trainable_engine%to_inference_engine(), string_t("initial-network.json"))

example/learn-power-series.f90

@@ -47,7 +47,9 @@ program learn_power_series
     type(bin_t), allocatable :: bins(:)
     real, allocatable :: cost(:), random_numbers(:)
 
+#ifndef NAGFOR
     call random_init(image_distinct=.true., repeatable=.true.)
+#endif
 
     trainable_engine = perturbed_identity_network(perturbation_magnitude=0.05)
     call output(trainable_engine%to_inference_engine(), string_t("initial-network.json"))

example/learn-saturated-mixing-ratio.f90

@@ -38,7 +38,9 @@ program train_saturated_mixture_ratio
     integer, parameter :: nodes_per_layer(*) = [2, 72, 1]
     real, parameter :: cost_tolerance = 1.E-08
 
+#ifndef NAGFOR
     call random_init(image_distinct=.true., repeatable=.true.)
+#endif
 
     open(newunit=network_unit, file=network_file%string(), form='formatted', status='old', iostat=io_status, action='read')
 

example/train-and-write.f90

@@ -34,7 +34,9 @@ program train_and_write
     type(bin_t), allocatable :: bins(:)
     real, allocatable :: cost(:), random_numbers(:)
 
+#ifndef NAGFOR
     call random_init(image_distinct=.true., repeatable=.true.)
+#endif
 
     trainable_engine = perturbed_identity_network(perturbation_magnitude=0.2)
     call output(trainable_engine%to_inference_engine(), string_t("initial-network.json"))

test/main.f90

@@ -1,9 +1,9 @@
 ! Copyright (c), The Regents of the University of California
 ! Terms of use are as specified in LICENSE.txt
 program main
-  use inference_engine_test_m, only : inference_engine_test_t  
-  use asymmetric_engine_test_m, only : asymmetric_engine_test_t  
-  use trainable_engine_test_m, only : trainable_engine_test_t  
+  use inference_engine_test_m, only : inference_engine_test_t
+  use asymmetric_engine_test_m, only : asymmetric_engine_test_t
+  use trainable_engine_test_m, only : trainable_engine_test_t
   use hyperparameters_test_m, only : hyperparameters_test_t
   use network_configuration_test_m, only : network_configuration_test_t
   use training_configuration_test_m, only : training_configuration_test_t
@@ -20,7 +20,9 @@ program main
   integer :: passes=0, tests=0
 
   call cpu_time(t_start)
+#ifndef NAGFOR
   call random_init(image_distinct=.true., repeatable=.true.)
+#endif
   call inference_engine_test%report(passes, tests)
   call asymmetric_engine_test%report(passes, tests)
   call trainable_engine_test%report(passes, tests)