If env_name is provided, it will return the information for the specified
environment as well.

Returns detailed information including:
- Conda version and configuration
- Python version and virtual packages
- Base environment location
- Channel URLs and package cache locations
- Platform and system details
- Complete list of all Conda environments with their paths
- Complete list of all packages in the specified environment and their versions

This is useful for diagnosing Conda-related issues or understanding
the Python environment configuration on this system.

Call the system_profiler with the given datatype. Allow LLM to deepdive into the
system information.
This function is used to get the system information to help user to understand the
system and potentially debug.

Allowed datatypes:
    - SPAirPortDataType - Airport/WiFi information
    - SPApplicationsDataType - Application information
    - SPAudioDataType - Audio device information
    - SPBluetoothDataType - Bluetooth information
    - SPCameraDataType - Camera information
    - SPDiagnosticsDataType - Diagnostic information
    - SPDisplaysDataType - Display and graphics information
    - SPFirewallDataType - Firewall settings
    - SPHardwareDataType - Hardware specifications
    - SPLocationDataType - Location services information
    - SPMemoryDataType - Memory information
    - SPNetworkDataType - Network settings and interfaces
    - SPNVMeDataType - NVMe storage details
    - SPPCIDataType - PCI devices information
    - SPPowerDataType - Battery and power information
    - SPSoftwareDataType - Software and OS information
    - SPStorageDataType - Storage devices and volumes
    - SPThunderboltDataType - Thunderbolt ports and connections
    - SPUSBDataType - USB devices and connections

Check if GPU is available in torch for a specific conda environment.
Input: torch or tensorflow
if framework is not provided, it will default to torch.

Returns a detailed dictionary with the following information:
- "torch_version": PyTorch version string
- "python_version": Python version string
- "platform": Platform information string
- "processor": Processor type
- "architecture": CPU architecture
- "mps_available": True if MPS (Metal Performance Shaders) is available
- "mps_built": True if PyTorch was built with MPS support
- "mps_functional": True if MPS is functional, False otherwise
- "benchmarks": A list of benchmark results for different matrix sizes, each containing:
  - "size": Matrix size used for benchmark
  - "cpu_time": Time taken on CPU (seconds)
  - "mps_time": Time taken on MPS (seconds)
  - "speedup": Ratio of CPU time to MPS time (higher means MPS is faster)

This helps determine if GPU acceleration via Apple's Metal is properly configured
and functioning, with performance benchmarks for comparison.