import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Softmax operation that normalizes the input tensor along a specified dimension.
    This operation is commonly used in neural networks for:
    - Converting logits to probabilities in classification tasks
    - Used in attention mechanisms to compute attention weights
    - Normalizing outputs to form probability distributions
    
    Formula: output_i = exp(input_i) / sum(exp(input_j)) for j in dimension
    """
    def __init__(self, dim=-1):
        super(Model, self).__init__()
        self.dim = dim

    def forward(self, input_tensor):
        # Softmax operation on input_tensor along the specified dimension
        result = torch.softmax(input_tensor, dim=self.dim)
        return result

def get_inputs():
    # Batch size: 32
    # Sequence length: 512
    # Hidden dimension: 4096
    input_tensor = torch.randn(32, 512, 4096, dtype=torch.float32)
    return [input_tensor]

def get_init_inputs():
    # Parameters for Softmax operation
    dim = -1  # Dimension along which to apply softmax
    return [dim]