So interestingly, if I don’t fit the data to the transform method as you indicated, and pass in a mean of 0 and std of 1, the program outputs the original data values (as expected) with the correct training and validation splits according to batch size, as follows:
[tensor([[ 0.0000e+00,  0.0000e+00,  4.0000e-01,  3.8540e+01,  8.5000e-01,
          2.3000e-01,  0.0000e+00,  1.3000e-01,  1.1000e-01,  2.0000e-02, 
          1.0000e-02,  1.1630e+01,  8.5000e-01,  1.6280e+01,  1.6500e+00, 
          8.1630e+01,  9.4200e+00,  8.0080e+01,  4.1800e+00, -2.3000e-01, 
          0.0000e+00,  1.3000e-01,  0.0000e+00,  3.0000e-02,  2.1000e-01, 
          2.7000e-01,  4.0000e-02,  1.5000e-01,  2.0000e-02,  2.0000e-02, 
          2.0000e-02,  3.0000e-02,  3.0000e-02,  2.3000e-01,  4.6000e-01,
          1.0000e-02, -4.0000e-02,  5.0000e-02,  2.0000e-02,  1.1590e+01,
          6.9000e-01,  3.9500e+00,  1.7000e-01,  1.7000e-01,  1.6000e-01,
          7.0000e-01],
        [ 0.0000e+00,  1.0000e-02,  1.7000e-01,  3.8990e+01,  3.2000e-01,
          5.6000e-01,  0.0000e+00,  1.4000e-01,  6.0000e-02,  0.0000e+00,
          1.0000e-02,  1.3290e+01,  8.9000e-01,  1.5110e+01,  1.4400e+00,
          8.3080e+01,  5.7800e+00,  8.2350e+01,  1.2100e+00, -2.1000e-01,
          0.0000e+00,  8.0000e-02, -1.0000e-02,  7.0000e-02,  1.5700e+00,
          2.9000e-01,  0.0000e+00,  8.0000e-02,  1.0000e-02,  1.0000e-02,
          1.0000e-02,  5.0000e-02,  5.0000e-02,  3.6000e-01,  5.2000e-01,
          1.1000e-01, -6.9000e-01,  5.0000e-02,  2.0000e-02,  1.3110e+01,
          7.1000e-01,  9.9000e-01,  8.8000e-01,  9.0000e-02,  1.5000e-01,
          9.9000e-01]]), tensor([97.5300, 90.6500], dtype=torch.float64)]

[tensor([[ 0.0000e+00,  0.0000e+00,  2.8000e-01,  3.9370e+01,  7.6000e-01,
          2.2000e-01,  0.0000e+00,  4.0000e-02,  1.2000e-01,  1.2000e-01,
          1.0000e-02,  1.0240e+01,  2.2000e-01,  1.3970e+01,  3.5900e+00,
          8.2260e+01,  1.3830e+01,  8.1620e+01,  1.3000e-01,  2.4900e+00,
          1.0000e-02, -1.0000e-02, -1.0000e-02, -1.0000e-02,  3.5000e-01,
          2.2000e-01,  2.4000e-01,  1.7000e-01,  3.0000e-02,  3.0000e-02,
          2.0000e-02,  2.0000e-02,  2.0000e-02,  1.5000e-01,  1.9100e+00,
          0.0000e+00, -1.4000e-01,  5.0000e-02,  1.0000e-02,  1.0210e+01,
          1.0000e-01,  2.6300e+00,  2.0000e-01,  1.9000e-01, -3.0000e-02,
          2.0600e+00],
        [ 1.0000e+00,  0.0000e+00,  2.0000e-01,  4.0670e+01,  1.8000e-01,
          2.0000e-02,  0.0000e+00,  4.0000e-02,  7.0000e-02,  1.0000e-02,
          1.0000e-02,  9.3900e+00,  1.8000e-01,  1.4200e+01,  3.1200e+00,
          8.3160e+01,  2.4400e+00,  8.6000e+01,  1.0000e-01,  1.0000e-02,
          0.0000e+00, -1.0000e-02,  0.0000e+00, -2.0000e-02,  4.0000e-02,
          1.0000e-02,  2.0000e-02,  4.0000e-02,  4.0000e-02,  4.0000e-02,
          3.0000e-02,  2.0000e-02,  2.0000e-02,  1.4000e-01,  1.3600e+00,
          0.0000e+00, -8.0000e-02,  5.0000e-02,  1.0000e-02,  9.3900e+00,
          1.7000e-01,  1.1000e-01, -3.0000e-02,  9.0000e-02, -3.0000e-02,
          1.5000e+00]]), tensor([88.9200, 16.4300], dtype=torch.float64)]

Recall, I should have 46 features and 1 target for the current dataset (which is shown correctly for a batch size of 2 above).
HOWEVER, when I fit the transform as follows:
class CSVDataset(Dataset):
    # Load the dataset.
    def __init__(self, file_path, train, transform=None):
        # Read the data into a dataframe.
        df = pd.read_csv(file_path)
        # Store the input & output variables.
        self.train_X = df.iloc[:, 1:-1].values.astype('float32')
        self.train_Y = df.iloc[:, -1].values.astype('float32')
        self.test_X = df.iloc[:, 1:-1].values.astype('float32')
        self.test_Y = np.empty(len(self.test_X))
        self.transform = transform
        self.train = train

        # Call fit on each data type.
        self.transform.fit(self.train_X)
        self.transform.fit(self.train_Y)
        self.transform.fit(self.test_X)

    def __getitem__(self, index):
        if self.train is True:
            X_data = self.train_X[index]
            Y_data = self.train_Y[index]
            if self.transform is not None:
                X_data = self.transform(X_data)
                Y_data = self.transform(Y_data)
            return X_data, Y_data

        else:
            X_data = self.test_X[index]
            if self.transform is not None:
                X_data = self.transform(X_data)
            return X_data

    def __len__(self):
        if self.train is True:
            return len(self.train_Y)
        else:
            return len(self.test_Y)

And remove the passed in mean and std:
# Create transform function to pass into CSVDataset class.
transform = StandardScaler()

In this instance/attempt, I get the correct transformed values for my features (which is great!), but the values for my target variable are now INCORRECT (and for some reason gives 46 values, when there should only be 2 for the current batch size) - see below:
[tensor([[[-1.1340, -0.5446, -0.5598,  0.2025, -0.6553, -0.7396, -0.5494,
          -0.3097, -0.8017, -0.5771, -0.4696,  1.2344,  1.3403,  1.6143,
          -1.1446, -0.9779, -0.9181,  0.4569, -0.3281, -0.8027, -0.2748,
           0.0803, -0.4945, -0.1579, -0.7164,  0.4976, -0.5771,  0.0457,
          -0.8130,  0.1791,  0.3221,  0.1381,  0.1431,  0.0120, -0.8547,
          -0.5386, -0.2114, -0.4859,  0.5818,  1.2470,  1.1203, -0.5967,
          -0.8299, -0.8094, -0.4081, -0.7897]],

        [[-1.1340, -0.4796, -1.0978,  0.3648, -0.9092, -0.2407, -0.5494,
          -0.2918, -1.0933, -1.0547, -0.4696,  1.7783,  1.4826,  1.3624,
          -1.1927, -0.4703, -1.0529,  0.6592, -0.8991, -0.7988, -0.2748,
          -0.0201, -0.5004,  0.0947,  0.4627,  0.5856, -1.0547, -0.6919,
          -0.8472, -0.7163, -0.5982,  0.2875,  0.2861,  0.1185, -0.7848,
          -0.4816, -0.8184, -0.4859,  0.5818,  1.7256,  1.1938, -0.8942,
          -0.2697, -1.0866, -0.4131, -0.6521]]]), tensor([[[1.9404e+02, 6.3300e+02, 2.2663e+02, 2.1479e+01, 4.5670e+01,
          1.4638e+02, 4.3743e+02, 1.7439e+02, 5.6724e+02, 2.3280e+03,
          5.8455e+03, 2.9376e+01, 3.4521e+02, 1.9109e+01, 2.0818e+01,
          4.5882e+00, 2.3434e+00, 2.0127e+00, 1.7619e+01, 1.8198e+01,
          2.6207e+02, 1.9558e+02, 5.7333e+01, 6.1546e+02, 8.3662e+01,
          4.2819e+02, 1.1635e+03, 1.0261e+03, 3.3252e+02, 8.7306e+03,
          8.9749e+03, 7.2811e+02, 6.9760e+02, 7.9742e+01, 1.1220e+02,
          5.5030e+01, 9.0906e+01, 1.0680e+03, 6.5462e+03, 2.8306e+01,
          3.5664e+02, 8.8077e+00, 7.5992e+01, 3.3645e+02, 4.8128e+01,
          4.5134e+01]],

        [[1.8027e+02, 5.8831e+02, 2.1053e+02, 1.8997e+01, 4.2373e+01,
          1.3598e+02, 4.0653e+02, 1.6205e+02, 5.2713e+02, 2.1637e+03,
          5.4331e+03, 2.7122e+01, 3.2074e+02, 1.7628e+01, 1.9242e+01,
          2.1797e+00, 2.0888e+00, 1.3993e+00, 1.6296e+01, 1.6860e+01,
          2.4356e+02, 1.8177e+02, 5.3254e+01, 5.7202e+02, 7.7697e+01,
          3.9794e+02, 1.0813e+03, 9.5357e+02, 3.0900e+02, 8.1146e+03,
          8.3417e+03, 6.7674e+02, 6.4838e+02, 7.4105e+01, 1.0418e+02,
          5.1109e+01, 8.4481e+01, 9.9259e+02, 6.0844e+03, 2.6139e+01,
          3.3139e+02, 8.1163e+00, 7.0563e+01, 3.1262e+02, 4.4699e+01,
          4.1871e+01]]])]

[tensor([[[-1.1340, -0.5446, -0.8405,  0.5018, -0.6984, -0.7547, -0.5494,
          -0.4711, -0.7434,  1.8109, -0.4696,  0.7791, -0.9005,  1.1169,
          -0.7002, -0.7574, -0.7549,  0.5942, -1.1068, -0.2740, -0.2479,
          -0.2007, -0.5004, -0.4104, -0.5951,  0.2778,  1.8109,  0.2564,
          -0.7788,  1.0744,  0.3221,  0.0635,  0.0715, -0.0535,  0.8341,
          -0.5443, -0.3048, -0.4859, -0.0895,  0.8125, -1.0457, -0.7294,
          -0.8063, -0.7402, -0.5028, -0.1447]],

        [[ 0.8672, -0.5446, -1.0276,  0.9707, -0.9763, -1.0572, -0.5494,
          -0.4711, -1.0350, -0.8159, -0.4696,  0.5006, -1.0427,  1.1664,
          -0.8079, -0.4423, -1.1765,  0.9847, -1.1125, -0.7560, -0.2748,
          -0.2007, -0.4945, -0.4736, -0.8638, -0.6457, -0.8159, -1.1133,
          -0.7447,  1.9697,  1.2425,  0.0635,  0.0715, -0.0617,  0.1935,
          -0.5443, -0.2487, -0.4859, -0.0895,  0.5543, -0.7887, -0.9826,
          -0.9878, -1.0866, -0.5028, -0.4102]]]), tensor([[[ 1.7681e+02,  5.7707e+02,  2.0649e+02,  1.8373e+01,  4.1544e+01,       
           1.3336e+02,  3.9876e+02,  1.5894e+02,  5.1704e+02,  2.1224e+03,
           5.3293e+03,  2.6556e+01,  3.1459e+02,  1.7255e+01,  1.8846e+01,
           1.5741e+00,  2.0247e+00,  1.2450e+00,  1.5963e+01,  1.6524e+01,
           2.3891e+02,  1.7830e+02,  5.2228e+01,  5.6110e+02,  7.6197e+01,
           3.9033e+02,  1.0607e+03,  9.3535e+02,  3.0309e+02,  7.9597e+03,
           8.1825e+03,  6.6382e+02,  6.3601e+02,  7.2687e+01,  1.0217e+02,
           5.0124e+01,  8.2866e+01,  9.7362e+02,  5.9682e+03,  2.5595e+01,
           3.2503e+02,  7.9425e+00,  6.9198e+01,  3.0663e+02,  4.3837e+01,
           4.1051e+01]],

        [[ 3.1745e+01,  1.0618e+02,  3.6934e+01, -7.7721e+00,  6.8101e+00,
           2.3755e+01,  7.3233e+01,  2.8926e+01,  9.4359e+01,  3.9130e+02,
           9.8385e+02,  2.8070e+00,  5.6755e+01,  1.6466e+00,  2.2410e+00,
          -2.3803e+01, -6.5864e-01, -5.2180e+00,  2.0269e+00,  2.4353e+00,
           4.3920e+01,  3.2797e+01,  9.2472e+00,  1.0339e+02,  1.3347e+01,
           7.1560e+01,  1.9512e+02,  1.7158e+02,  5.5284e+01,  1.4694e+03,
           1.5107e+03,  1.2259e+02,  1.1746e+02,  1.3287e+01,  1.7745e+01,
           8.8178e+00,  1.5169e+01,  1.7906e+02,  1.1021e+03,  2.7709e+00,
           5.8906e+01,  6.5746e-01,  1.2000e+01,  5.5516e+01,  7.7021e+00,
           6.6707e+00]]])]

Any idea what I’m doing wrong now??? Sorry about this, but I really appreciate your help and it appears I’m almost where I need to be in order to begin building my ANN, so thank you!!!
Thanks in advance and cheers