Project: 056_Learning_stochastic_dynamics_from_snapshots_throug | Function: cal_mass_loss (iclr)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Calculates local mass matching loss for unbalanced optimal transport. Computes the mass conservation loss by comparing the predicted mass distribution (from the weight vector lnw_t_last) with the empirical distribution of closest points between data_t1 and x_t_last. This ensures that the learned dynamics preserve local mass constraints in the unbalanced optimal transport framework.

**KEY TERMS:** {
  "weights": "particle weights w_i(t)",
  "count": "particle counts",
  "relative_count": "normalized counts",
  "local_mass_loss": "LMass"
}

**CONTEXT CODE:**         drop_index (bool): Whether or not to drop index from `df`. Defaults to `False`.

    Returns
    -------
        counts_n (pd.DataFrame): subsetted `df` where all rows correspond to `sample==n`.
    '''
    if groups is None:
        groups =  get_groups_from_df(df, samples_key, samples)
        
    try:
        counts_n = df.reset_index(drop=drop_index)[df[samples_key] == groups[n]]
    except KeyError:
        if samples is not None:
            counts_n = df.reset_index(drop=drop_index)[samples == groups[n]]
        else:
            raise ValueError(
                f'DataFrame df has no key {samples_key} and backup list of samples'
                f' samples is None.'
            )
    return counts_n

def get_times_from_groups(groups, where='start', start=0):
    '''
    Arguments
    ---------
        groups (list): the list of the numerical groups in the data, e.g. 
            `[0, 1, 2, 3, 4]`, if the data has five groups.
        
        where (str): Choices are `"start"`, and `"end"`. Defaults to `"end"`. Whether or not
            to start the trajectories at `t_0` (`"start"`) or `t_n` (`"end"`). 
    
        start (int): Defaults to `0`. Where in `generate_tjnet_trajectories` the trajectories started.
            This is used if attempting to generate outside of `t0`. Note this works relative to `where`.
            E.g. if `where="end"` and `start=0` then this is the same as `groups[-1]`.

    Returns
    -------
        times (list): The `groups` starting at `start` working from `end`.
    '''
    _valid_where = 'start end'.split()
    if where not in _valid_where:
        raise ValueError(f'{where} not known. Should be one of {_valid_where}')

    times = groups
    if where == 'end':
        times = times[::-1]
    times = times[start:]
    return times

# %%
def cal_mass_loss(data_t1, x_t_last, lnw_t_last, relative_mass, batch_size):
    raise NotImplementedError("Function implementation has been masked for testing")
# %%
# define lookup variables
try:
    from .losses import MMD_loss, OT_loss1, OT_loss2, Density_loss, Local_density_loss
except ImportError:
    # Mock imports for testing
    MMD_loss = None
    OT_loss1 = None
    OT_loss2 = None
    Density_loss = None
    Local_density_loss = None
from DeepRUOT.constants import ROOT_DIR, DATA_DIR, NTBK_DIR, IMGS_DIR, RES_DIR

_valid_datasets = {
    'file': lambda file: np.load(file),
}

_valid_criterions = {
    'mmd': MMD_loss,
    'ot1': OT_loss1,
    'ot2': OT_loss2,
}

import argparse
import sys


# Define the parser
parser = argparse.ArgumentParser(prog='DeepRUOT Training', description='Train DeepRUOT')

# NOTE: Dataset specification
parser.add_argument(
    '--dataset', '-d', type=str, choices=list(_valid_datasets.keys()), required=True,
    help=(
        'Dataset of the experiment to use. '
        'If value is fullpath to a file then tries to load file. '
        'Note, if using your own file we assume it is a pandas '
        'dataframe which has a column named `samples` that correspond to '
        'the timepoints.'
    )
)

parser.add_argument(
    '--time-col', '-tc', type=str, choices='simulation_i step_ix sim_time'.split(), required=False,
    help='Time column of the dataset to use.'
)

# NOTE: Experiment specification
parser.add_argument(
    '--name', '-n', type=str, required=True, default=None,
    help='Name of the experiment. If none is provided timestamp is used.'
)

parser.add_argument(
    '--output-dir', '-od', type=str, default=RES_DIR,
    help='Where experiments should be saved. The results directory will automatically be generated here.'
)

# NOTE: Runtime arguments
parser.add_argument(
    '--local-epochs', '-le', type=int, default=5,
    help='Number of epochs to use `local_loss` while training. These epochs occur first. Defaults to `5`.'
)
parser.add_argument(
    '--epochs', '-e', type=int, default=15,
    help='Number of epochs to use `global_loss` while training. Defaults to `15`.' 
)
parser.add_argument(
    '--local-post-epochs', '-lpe', type=int, default=5,
    help='Number of epochs to use `local_loss` after training. These epochs occur last. Defaults to `5`.'
)

# NOTE: Train arguments
parser.add_argument(
    '--criterion', '-c', type=str, choices=list(_valid_criterions.keys()), 
    default='mmd', required=True,
    help='a loss function, either `"mmd"` or `"emd"`. Defaults to `"mmd"`.'
)

parser.add_argument(
    '--batches', '-b', type=int, default=100,
    help='the number of batches from which to randomly sample each consecutive pair of groups.'
)

parser.add_argument(
    '--cuda', '--use-gpu', '-g',
    action='store_true', default=True,
    help='Whether or not to use CUDA. Defaults to `True`.'
)

parser.add_argument(
    '--sample-size', '-ss', type=int, default=100,     
    help='Number of points to sample during each batch. Defaults to `100`.'
)

parser.add_argument(
    '--sample-with-replacement', '-swr',
    action='store_true', default=False,


Complete the function implementation replacing "raise NotImplementedError".